https://www.na-mic.org/w/api.php?action=feedcontributions&user=Kubicki&feedformat=atomNAMIC Wiki - User contributions [en]2024-03-28T15:06:54ZUser contributionsMediaWiki 1.33.0https://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60860DBP2:HarvardFinal:20102010-11-12T18:04:03Z<p>Kubicki: /* Short overview */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography. <br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
[[Image:Slide08.png|thumb|right|400px|]]<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography module is currently part of Slicer 3.6 release. The software, when provided two "seeding" regions, finds the probability of connection between those regions, by calculating streamline tracts with a small amount of variation introduced at each step. These tracts are then averaged, to produce a "stochastic" cloud. The module is currently python based. Module is flexible, containing options for data smoothing, filtering, thresholding, changing tractography settings (such as total number of tracts, maximum length, step size, spacing, stopping), as well as types of output (connectivity map, FA, Mode, Trace). Inputs and outputs are compatible with other slicer modules, and can be visualized in Slicer (including 3D volume rendering). End to end tutorial for running stochastic tractography can be found here:<br />
<br />
Tutorial ([[Media:Stochastic Tractography TutorialContestSummer2010.pdf|PDF - 1MB]])<br />
<br />
Tutorial ([[Media:Stochastic_Tractography_TutorialContestSummer2010.ppt|PPT - 1.2MB]])<br />
<br />
= Listing and short description of the sample data =<br />
Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.<br />
Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.<br />
Sample data set can be found here:<br />
<br />
Dataset ([[Media:Stochastic_tutorial_data_TutorialContestSummer2010.zip|ZIP - 92MB]])</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60859DBP2:HarvardFinal:20102010-11-12T18:03:43Z<p>Kubicki: /* Short overview */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography. <br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
[[Image:Slide08.png|thumb|right|200px|]]<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography module is currently part of Slicer 3.6 release. The software, when provided two "seeding" regions, finds the probability of connection between those regions, by calculating streamline tracts with a small amount of variation introduced at each step. These tracts are then averaged, to produce a "stochastic" cloud. The module is currently python based. Module is flexible, containing options for data smoothing, filtering, thresholding, changing tractography settings (such as total number of tracts, maximum length, step size, spacing, stopping), as well as types of output (connectivity map, FA, Mode, Trace). Inputs and outputs are compatible with other slicer modules, and can be visualized in Slicer (including 3D volume rendering). End to end tutorial for running stochastic tractography can be found here:<br />
<br />
Tutorial ([[Media:Stochastic Tractography TutorialContestSummer2010.pdf|PDF - 1MB]])<br />
<br />
Tutorial ([[Media:Stochastic_Tractography_TutorialContestSummer2010.ppt|PPT - 1.2MB]])<br />
<br />
= Listing and short description of the sample data =<br />
Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.<br />
Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.<br />
Sample data set can be found here:<br />
<br />
Dataset ([[Media:Stochastic_tutorial_data_TutorialContestSummer2010.zip|ZIP - 92MB]])</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60858DBP2:HarvardFinal:20102010-11-12T18:01:24Z<p>Kubicki: /* Short overview */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography. <br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
[[File:[[File:Slide08.png]]]]<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography module is currently part of Slicer 3.6 release. The software, when provided two "seeding" regions, finds the probability of connection between those regions, by calculating streamline tracts with a small amount of variation introduced at each step. These tracts are then averaged, to produce a "stochastic" cloud. The module is currently python based. Module is flexible, containing options for data smoothing, filtering, thresholding, changing tractography settings (such as total number of tracts, maximum length, step size, spacing, stopping), as well as types of output (connectivity map, FA, Mode, Trace). Inputs and outputs are compatible with other slicer modules, and can be visualized in Slicer (including 3D volume rendering). End to end tutorial for running stochastic tractography can be found here:<br />
<br />
Tutorial ([[Media:Stochastic Tractography TutorialContestSummer2010.pdf|PDF - 1MB]])<br />
<br />
Tutorial ([[Media:Stochastic_Tractography_TutorialContestSummer2010.ppt|PPT - 1.2MB]])<br />
<br />
= Listing and short description of the sample data =<br />
Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.<br />
Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.<br />
Sample data set can be found here:<br />
<br />
Dataset ([[Media:Stochastic_tutorial_data_TutorialContestSummer2010.zip|ZIP - 92MB]])</div>Kubickihttps://www.na-mic.org/w/index.php?title=File:Slide08.png&diff=60857File:Slide08.png2010-11-12T18:01:03Z<p>Kubicki: </p>
<hr />
<div></div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60849DBP2:HarvardFinal:20102010-11-12T17:25:56Z<p>Kubicki: /* Listing and short description of the sample data */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography. <br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography module is currently part of Slicer 3.6 release. The software, when provided two "seeding" regions, finds the probability of connection between those regions, by calculating streamline tracts with a small amount of variation introduced at each step. These tracts are then averaged, to produce a "stochastic" cloud. The module is currently python based. Module is flexible, containing options for data smoothing, filtering, thresholding, changing tractography settings (such as total number of tracts, maximum length, step size, spacing, stopping), as well as types of output (connectivity map, FA, Mode, Trace). Inputs and outputs are compatible with other slicer modules, and can be visualized in Slicer (including 3D volume rendering). End to end tutorial for running stochastic tractography can be found here:<br />
<br />
Tutorial ([[Media:Stochastic Tractography TutorialContestSummer2010.pdf|PDF - 1MB]])<br />
<br />
Tutorial ([[Media:Stochastic_Tractography_TutorialContestSummer2010.ppt|PPT - 1.2MB]])<br />
<br />
= Listing and short description of the sample data =<br />
Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.<br />
Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.<br />
Sample data set can be found here:<br />
<br />
Dataset ([[Media:Stochastic_tutorial_data_TutorialContestSummer2010.zip|ZIP - 92MB]])</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60848DBP2:HarvardFinal:20102010-11-12T17:25:44Z<p>Kubicki: /* Listing and short description of the sample data */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography. <br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography module is currently part of Slicer 3.6 release. The software, when provided two "seeding" regions, finds the probability of connection between those regions, by calculating streamline tracts with a small amount of variation introduced at each step. These tracts are then averaged, to produce a "stochastic" cloud. The module is currently python based. Module is flexible, containing options for data smoothing, filtering, thresholding, changing tractography settings (such as total number of tracts, maximum length, step size, spacing, stopping), as well as types of output (connectivity map, FA, Mode, Trace). Inputs and outputs are compatible with other slicer modules, and can be visualized in Slicer (including 3D volume rendering). End to end tutorial for running stochastic tractography can be found here:<br />
<br />
Tutorial ([[Media:Stochastic Tractography TutorialContestSummer2010.pdf|PDF - 1MB]])<br />
<br />
Tutorial ([[Media:Stochastic_Tractography_TutorialContestSummer2010.ppt|PPT - 1.2MB]])<br />
<br />
= Listing and short description of the sample data =<br />
Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.<br />
Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.<br />
Sample data set can be found here:<br />
Dataset ([[Media:Stochastic_tutorial_data_TutorialContestSummer2010.zip|ZIP - 92MB]])</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60847DBP2:HarvardFinal:20102010-11-12T17:25:02Z<p>Kubicki: /* Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography. <br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography module is currently part of Slicer 3.6 release. The software, when provided two "seeding" regions, finds the probability of connection between those regions, by calculating streamline tracts with a small amount of variation introduced at each step. These tracts are then averaged, to produce a "stochastic" cloud. The module is currently python based. Module is flexible, containing options for data smoothing, filtering, thresholding, changing tractography settings (such as total number of tracts, maximum length, step size, spacing, stopping), as well as types of output (connectivity map, FA, Mode, Trace). Inputs and outputs are compatible with other slicer modules, and can be visualized in Slicer (including 3D volume rendering). End to end tutorial for running stochastic tractography can be found here:<br />
<br />
Tutorial ([[Media:Stochastic Tractography TutorialContestSummer2010.pdf|PDF - 1MB]])<br />
<br />
Tutorial ([[Media:Stochastic_Tractography_TutorialContestSummer2010.ppt|PPT - 1.2MB]])<br />
<br />
= Listing and short description of the sample data =<br />
Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.<br />
Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60846DBP2:HarvardFinal:20102010-11-12T17:10:03Z<p>Kubicki: /* Short overview */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography. <br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
<br />
= Listing and short description of the sample data =<br />
Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.<br />
Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60823DBP2:HarvardFinal:20102010-11-12T00:42:29Z<p>Kubicki: /* Short overview */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia.<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
<br />
= Listing and short description of the sample data =<br />
Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.<br />
Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60039DBP2:HarvardFinal:20102010-10-30T20:56:53Z<p>Kubicki: /* Listing and short description of the sample data */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
<br />
= Listing and short description of the sample data =<br />
Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.<br />
Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60038DBP2:HarvardFinal:20102010-10-30T20:49:36Z<p>Kubicki: /* Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
<br />
Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.<br />
<br />
= Listing and short description of the sample data =</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60026DBP2:HarvardFinal:20102010-10-29T21:44:49Z<p>Kubicki: /* Related publications */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
</small><br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
= Listing and short description of the sample data =</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60025DBP2:HarvardFinal:20102010-10-29T21:43:28Z<p>Kubicki: /* Related publications */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
= Related publications =<br />
<small><br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
= Listing and short description of the sample data =</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60024DBP2:HarvardFinal:20102010-10-29T21:42:24Z<p>Kubicki: /* Related publications */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
= Related publications =<br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
<br />
= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =<br />
= Listing and short description of the sample data =</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60022DBP2:HarvardFinal:20102010-10-29T21:34:53Z<p>Kubicki: /* Related publications */</p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
= Related publications =<br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF, Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008): Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME. (2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum. Schizophr Res 106(2 3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA, Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia. Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia: A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME. (2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009): Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D, Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
<br />
*Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish<br />
*Listing and short description of the sample data</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:HarvardFinal:2010&diff=60021DBP2:HarvardFinal:20102010-10-29T21:32:22Z<p>Kubicki: </p>
<hr />
<div> [[DBP2:Main|back to DBP2 Main]]<br />
<br />
= Short overview =<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
= Related publications =<br />
* Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.<br />
* Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006<br />
* Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. (2007): Finsler<br />
tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput<br />
Assist Interv Int Conf Med Image Comput Comput Assist Interv 10(Pt 1):36-43.<br />
* Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell GB, Markant D, Niznikiewicz M, Westin CF,<br />
Kikinis R, J Saykin A, McCarley RW, Shenton ME. (2008): Age-related deficits in fronto-temporal<br />
connections in schizophrenia: a diffusion tensor imaging study. Schizophr Res Jul;102(1-3):181-188.<br />
* Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. (2008):<br />
Neuropsychological disturbance in schizophrenia: a diffusion tensor imaging study. Neuropsychology<br />
Mar;22(2):246-254.<br />
* Aja-Fernandez S, Niethammer M, Kubicki M, Shenton, ME, Westin, C-F. (2008): Restoration of DWI<br />
Data Using a Rician LMMSE Estimator. IEEE Trans Med Imaging Oct; 27(10):1389-403.<br />
* Kubicki M, Styner M, Bouix S, Gerig G, Markant D, Smith K, Kikinis R, McCarley RW, Shenton ME.<br />
(2008): Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the<br />
Corpus Callosum. Schizophr Res 106(2-3):125-31.<br />
* Maddah M, Kubicki M, Wells WM, Westin CF, Shenton ME, Grimson WE. (2008): Findings in<br />
schizophrenia by tract-oriented DT-MRI analysis. Med Image Comput Comput Assist Interv Int Conf Med<br />
Image Comput Comput Assist Interv (MICCAI) 11(Pt 1):917-24.<br />
* Fitzsimmons J, Kubicki M, Smith K, Bushell G, Estepar RS, Westin CF, Nestor PG, Niznikiewicz MA,<br />
Kikinis R, McCarley RW, Shenton ME. (2009): Diffusion tractography of the fornix in schizophrenia.<br />
Schizophr Res Jan;107(1):39-46.<br />
* Lee K, Yoshida T, Kubicki M, Bouix S, Westin CF, Kindlmann G, Niznikiewicz M, Cohen A, McCarley<br />
RW, Shenton ME. (2009): Increased diffusivity in superior temporal gyrus in patients with schizophrenia:<br />
A Diffusion Tensor Imaging study. Schizophr Res Jan 8. [Epub ahead of print].<br />
* Kawashima T, Nakamura M, Bouix S, Kubicki M, Salisbury D, Westin CF, McCarley RW, Shenton ME.<br />
(2009): Uncinate fasciculus abnormalities in recent onset schizophrenia and affective psychosis: A<br />
diffusion tensor imaging study: Schizophr Res 110: 119-126<br />
* Oh JS, Kubicki M, Rosenberger G, Bouix S, Levitt JL, McCarley RW, Westin C-F, Shenton ME. (2009):<br />
Thalamo-Frontal White Matter Alterations in Chronic Schizophrenia: A Quantitative Diffusion<br />
Tractography Study. Hum Brain Mapp. Nov;30(11):3812-25.<br />
* Jeong BS, Wible CG, Hashimoto RH, Kubicki M. (2009): Functional and Anatomical Connectivity<br />
Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia. Hum Brain Mapp. Dec;30(12):4138-51.<br />
* Ungar L, Niznikiewicz M, Nestor P, Kubicki M. (2010): Color Stroop and Negative Priming in<br />
Schizophrenia: An fMRI Study. Psychiatry Res Jan 30;181(1):24-9.<br />
* Jeong BS, Kubicki M. (2010): Reduced Task-related Suppression during Semantic Repetition Priming in<br />
Schizophrenia. Psychiatry Res. Feb 28;181(2):114-20.<br />
* Nestor PG, Kubicki M, Nakamura M, Niznikiewicz M, McCarley RW, Shenton ME. (2010): Comparing<br />
prefrontal gray and white matter contributions to intelligence and decision making in schizophrenia and<br />
healthy controls. Neuropsychology. Jan;24(1):121-9.<br />
* Whitford TJ, Kubicki M, Schneiderman JS, O'Donnell LJ, King R, Alvarado JL, Khan U, Markant D,<br />
Nestor PG, Niznikiewicz M, McCarley RW, Westin CF, Shenton ME. (2010): Corpus Callosum<br />
Abnormalities and Their Association with Psychotic Symptoms in Patients with Schizophrenia. Biol<br />
Psychiatry. May 20. [Epub ahead of print]<br />
* Kubicki M, Niznikiewicz M, Connor E, Ungar L, Nestor PG, Bouix S, Dreusicke M, Kikinis R, McCarley<br />
RW, Shenton ME. (In Press): Relationship Between White Matter Integrity, Attention, and Memory in<br />
Schizophrenia: A Diffusion Tensor Imaging Study. Brain Imaging and Behavior.<br />
<br />
*Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish<br />
*Listing and short description of the sample data</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60020DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T21:23:11Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Algorithm has been optimized to work on higher resolution data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
:* Separate components of work flow have been tested, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* Module have been tested on other Philips and GE datasets. <br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 3). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference)<br />
<br />
== Related Clinical Projects ==<br />
<br />
;* Arcuate Fasciculus Extraction and Analysis in Schizophrenia <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We are investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts within both ROIs. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper has been submitted to Neuroimage.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity in Schizophrenia<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network in Psychosis<br />
We are collaborating with the Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person), where we use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. The fMRI resting state results (ROIs) have been co-registered with anatomical scans, and then again registered to DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. <br />
:* Results presentation and paper submission. We are in the process of analyzing results.<br />
<br />
;* Study of OFC-ACC connectivity in Chronic Schizophrenia<br />
We are using stochastic tractography to examine white matter connectivity between medial anterior and posterior OFC and rostral ACC (cognitive part of ACC). <br />
:* Results presentation and paper submission. Data was analyzed for 27 SZ and 256 HC subjects. Schizophrenia group demonstrated significant mean FA reduction in the connection between left anterior OFC and ACC, and between bilateral posterior OFC. Poster was presented at Annual Biological Psychiatry Meeting, paper in preparation.<br />
<br />
;* Study of thalamus segmentation based on cortical connectivity in Chronic Schizophrenia<br />
We use stochastic tractography to segment thalamus into discrete ROIs based on its connectivity to 10 cortical ROIs (for each hemisphere) of the frontal lobe. Cortical ROIs are extracted using free surfer, and co-registered to DTI space using FSL. Thalamus is painted by connectivity using additional in-house matlab script (available after request). Volumes of the thalamic ROIs as well as FA for each individual connection are our measures of interest. <br />
:* Results presentation and paper submission. Data analysis and paper in preparation.<br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
; - Related References<br />
<br />
:* Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.<br />
:* Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* T. Otani, M Kubicki, S. Bouix, P Nestor, A Rausch, T Asami, D, Terry, E Melonakos, K Hawley, P Pelavin, J Alvarado, A LaVenture, J Siebenthal, R McCarley, and M Shenton. White matter connections between orbitofrontal cortex and anterior cingulate cortex in shchizophrenia, Annual Meeting, Society of Biological Psychiatry, 2010.<br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Ryan are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Andrew Rauch is our NAMIC RA. <br />
* Ryan is our NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
* '''01/2010''' - AHM progress presentation.<br />
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco. <br />
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release. <br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60019DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T21:21:25Z<p>Kubicki: /* Work Accomplished */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Algorithm has been optimized to work on higher resolution data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
:* Separate components of work flow have been tested, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* Module have been tested on other Philips and GE datasets. <br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 3). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference)<br />
<br />
== Work in Progress ==<br />
<br />
; - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction and Analysis in Schizophrenia <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We are investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts within both ROIs. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper has been submitted to Neuroimage.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity in Schizophrenia<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network in Psychosis<br />
We are collaborating with the Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person), where we use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. The fMRI resting state results (ROIs) have been co-registered with anatomical scans, and then again registered to DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. <br />
:* Results presentation and paper submission. We are in the process of analyzing results.<br />
<br />
;* Study of OFC-ACC connectivity in Chronic Schizophrenia<br />
We are using stochastic tractography to examine white matter connectivity between medial anterior and posterior OFC and rostral ACC (cognitive part of ACC). <br />
:* Results presentation and paper submission. Data was analyzed for 27 SZ and 256 HC subjects. Schizophrenia group demonstrated significant mean FA reduction in the connection between left anterior OFC and ACC, and between bilateral posterior OFC. Poster was presented at Annual Biological Psychiatry Meeting, paper in preparation.<br />
<br />
;* Study of thalamus segmentation based on cortical connectivity in Chronic Schizophrenia<br />
We use stochastic tractography to segment thalamus into discrete ROIs based on its connectivity to 10 cortical ROIs (for each hemisphere) of the frontal lobe. Cortical ROIs are extracted using free surfer, and co-registered to DTI space using FSL. Thalamus is painted by connectivity using additional in-house matlab script (available after request). Volumes of the thalamic ROIs as well as FA for each individual connection are our measures of interest. <br />
:* Results presentation and paper submission. Data analysis and paper in preparation.<br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Ryan are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Andrew Rauch is our NAMIC RA. <br />
* Ryan is our NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
* '''01/2010''' - AHM progress presentation.<br />
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco. <br />
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release. <br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60018DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T21:08:47Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Algorithm has been optimized to work on higher resolution data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
:* Separate components of work flow have been tested, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* Module have been tested on other Philips and GE datasets. <br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 3). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* T. Otani, M Kubicki, S. Bouix, P Nestor, A Rausch, T Asami, D, Terry, E Melonakos, K Hawley, P Pelavin, J Alvarado, A LaVenture, J Siebenthal, R McCarley, and M Shenton. White matter connections between orbitofrontal cortex and anterior cingulate cortex in shchizophrenia, Annual Meeting, Society of Biological Psychiatry, 2010.<br />
<br />
== Work in Progress ==<br />
<br />
; - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction and Analysis in Schizophrenia <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We are investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts within both ROIs. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper has been submitted to Neuroimage.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity in Schizophrenia<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network in Psychosis<br />
We are collaborating with the Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person), where we use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. The fMRI resting state results (ROIs) have been co-registered with anatomical scans, and then again registered to DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. <br />
:* Results presentation and paper submission. We are in the process of analyzing results.<br />
<br />
;* Study of OFC-ACC connectivity in Chronic Schizophrenia<br />
We are using stochastic tractography to examine white matter connectivity between medial anterior and posterior OFC and rostral ACC (cognitive part of ACC). <br />
:* Results presentation and paper submission. Data was analyzed for 27 SZ and 256 HC subjects. Schizophrenia group demonstrated significant mean FA reduction in the connection between left anterior OFC and ACC, and between bilateral posterior OFC. Poster was presented at Annual Biological Psychiatry Meeting, paper in preparation.<br />
<br />
;* Study of thalamus segmentation based on cortical connectivity in Chronic Schizophrenia<br />
We use stochastic tractography to segment thalamus into discrete ROIs based on its connectivity to 10 cortical ROIs (for each hemisphere) of the frontal lobe. Cortical ROIs are extracted using free surfer, and co-registered to DTI space using FSL. Thalamus is painted by connectivity using additional in-house matlab script (available after request). Volumes of the thalamic ROIs as well as FA for each individual connection are our measures of interest. <br />
:* Results presentation and paper submission. Data analysis and paper in preparation.<br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Ryan are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Andrew Rauch is our NAMIC RA. <br />
* Ryan is our NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
* '''01/2010''' - AHM progress presentation.<br />
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco. <br />
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release. <br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60017DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T20:55:27Z<p>Kubicki: /* Staffing Plan */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Algorithm has been optimized to work on higher resolution data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
:* Separate components of work flow have been tested, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* Module have been tested on other Philips and GE datasets. <br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 3). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* T. Otani, M Kubicki, S. Bouix, P Nestor, A Rausch, T Asami, D, Terry, E Melonakos, K Hawley, P Pelavin, J Alvarado, A LaVenture, J Siebenthal, R McCarley, and M Shenton. White matter connections between orbitofrontal cortex and anterior cingulate cortex in shchizophrenia, Annual Meeting, Society of Biological Psychiatry, 2010.<br />
<br />
== Work in Progress ==<br />
<br />
; - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We are investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts within both ROIs. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper has been submitted to Neuroimage.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We are collaborating with the Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person), where we use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. The fMRI resting state results (ROIs) have been co-registered with anatomical scans, and then again registered to DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. <br />
:* Results presentation and paper submission. We are in the process of analyzing results.<br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Ryan are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Andrew Rauch is our NAMIC RA. <br />
* Ryan is our NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
* '''01/2010''' - AHM progress presentation.<br />
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco. <br />
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release. <br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60016DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T20:54:54Z<p>Kubicki: /* Staffing Plan */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Algorithm has been optimized to work on higher resolution data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
:* Separate components of work flow have been tested, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* Module have been tested on other Philips and GE datasets. <br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 3). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* T. Otani, M Kubicki, S. Bouix, P Nestor, A Rausch, T Asami, D, Terry, E Melonakos, K Hawley, P Pelavin, J Alvarado, A LaVenture, J Siebenthal, R McCarley, and M Shenton. White matter connections between orbitofrontal cortex and anterior cingulate cortex in shchizophrenia, Annual Meeting, Society of Biological Psychiatry, 2010.<br />
<br />
== Work in Progress ==<br />
<br />
; - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We are investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts within both ROIs. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper has been submitted to Neuroimage.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We are collaborating with the Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person), where we use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. The fMRI resting state results (ROIs) have been co-registered with anatomical scans, and then again registered to DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. <br />
:* Results presentation and paper submission. We are in the process of analyzing results.<br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Andrew Rauch, our NAMIC RA is our NAMIC RA. <br />
* Ryan is our NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
* '''01/2010''' - AHM progress presentation.<br />
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco. <br />
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release. <br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60015DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T20:53:11Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Algorithm has been optimized to work on higher resolution data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
:* Separate components of work flow have been tested, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* Module have been tested on other Philips and GE datasets. <br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 3). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* T. Otani, M Kubicki, S. Bouix, P Nestor, A Rausch, T Asami, D, Terry, E Melonakos, K Hawley, P Pelavin, J Alvarado, A LaVenture, J Siebenthal, R McCarley, and M Shenton. White matter connections between orbitofrontal cortex and anterior cingulate cortex in shchizophrenia, Annual Meeting, Society of Biological Psychiatry, 2010.<br />
<br />
== Work in Progress ==<br />
<br />
; - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We are investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts within both ROIs. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper has been submitted to Neuroimage.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We are collaborating with the Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person), where we use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. The fMRI resting state results (ROIs) have been co-registered with anatomical scans, and then again registered to DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. <br />
:* Results presentation and paper submission. We are in the process of analyzing results.<br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Andrew Rauch, our NAMIC RA is our NAMIC RA. <br />
* Ryan is our NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
* '''01/2010''' - AHM progress presentation.<br />
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco. <br />
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release. <br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60014DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T20:35:10Z<p>Kubicki: /* Work Accomplished */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Algorithm has been optimized to work on higher resolution data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
:* Separate components of work flow have been tested, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* Module have been tested on other Philips and GE datasets. <br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 3). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* T. Otani, M Kubicki, S. Bouix, P Nestor, A Rausch, T Asami, D, Terry, E Melonakos, K Hawley, P Pelavin, J Alvarado, A LaVenture, J Siebenthal, R McCarley, and M Shenton. White matter connections between orbitofrontal cortex and anterior cingulate cortex in shchizophrenia, Annual Meeting, Society of Biological Psychiatry, 2010.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60013DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T20:32:02Z<p>Kubicki: /* Work Accomplished */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Algorithm has been optimized to work on higher resolution data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 3). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* T. Otani, M Kubicki, S. Bouix, P Nestor, A Rausch, T Asami, D, Terry, E Melonakos, K Hawley, P Pelavin, J Alvarado, A LaVenture, J Siebenthal, R McCarley, and M Shenton. White matter connections between orbitofrontal cortex and anterior cingulate cortex in shchizophrenia, Annual Meeting, Society of Biological Psychiatry, 2010.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60012DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T20:30:59Z<p>Kubicki: /* Work Accomplished */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Algorithm has been optimized to work on higher resolution data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* T. Otani, M Kubicki, S. Bouix, P Nestor, A Rausch, T Asami, D, Terry, E Melonakos, K Hawley, P Pelavin, J Alvarado, A LaVenture, J Siebenthal, R McCarley, and M Shenton. White matter connections between orbitofrontal cortex and anterior cingulate cortex in shchizophrenia, Annual Meeting, Society of Biological Psychiatry, 2010.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=60011DBP2:Harvard:Brain Segmentation Roadmap2010-10-29T19:19:31Z<p>Kubicki: /* Work Accomplished */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated. If Enabled, will use threshold values on the baseline instead of WM Mask defined in IO panel.<br />
*IJK/RAS Switch<br />
Chooses the way the nhdrs are read.<br />
*Diffusion Tensor:<br />
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. Total Tracts: The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length: (in mm) This can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size(mm): distance between each re-estimation of tensors, usually between 0.5 and 1 mm. Adjust to make sure step size is not larger than the voxel spacing in any direction, which would allow voxels to be "jumped over."<br />
:4. Stopping criteria: This can be used on the top of WM mask to terminate tracts when FA drops below supplied threshold (in case they frequently travel through CSF, for example). <br />
:5. Use Basic Method: switches between Friman and McGraw algorithms.<br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value.<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, de Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* T. Otani, M Kubicki, S. Bouix, P Nestor, A Rausch, T Asami, D, Terry, E Melonakos, K Hawley, P Pelavin, J Alvarado, A LaVenture, J Siebenthal, R McCarley, and M Shenton. White matter connections between orbitofrontal cortex and anterior cingulate cortex in shchizophrenia, Annual Meeting, Society of Biological Psychiatry, 2010.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=File:NA-MIC_Kubicki_2010.ppt&diff=47026File:NA-MIC Kubicki 2010.ppt2010-01-05T17:18:13Z<p>Kubicki: </p>
<hr />
<div></div>Kubickihttps://www.na-mic.org/w/index.php?title=AHM2010:PNL&diff=47025AHM2010:PNL2010-01-05T17:17:43Z<p>Kubicki: Created page with 'Media:NA-MIC_Kubicki_2010.ppt'</p>
<hr />
<div>[[Media:NA-MIC_Kubicki_2010.ppt]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38714DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T18:28:05Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
[[Image:connectivity1.jpg|thumb|right|200px|<font size=1>Figure 8: Maps of inferior frontal cortex (Broca) connectivity in controls and patients with schizophrenia.</font>]]<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 10: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=File:Connectivity1.jpg&diff=38713File:Connectivity1.jpg2009-06-15T18:25:26Z<p>Kubicki: </p>
<hr />
<div></div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38712DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T18:15:38Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 8: Brain automatic segmentation of subject with VCFS.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 9: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=File:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg&diff=38711File:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg2009-06-15T18:12:15Z<p>Kubicki: </p>
<hr />
<div></div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38699DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T15:24:21Z<p>Kubicki: /* Schedule */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
* '''12/2009''' - Submission of abstracts reporting findings of several clinical studies involving stochastic tractography, including anatomical connectivity abnormalities in patients with VCFS and anatomical and functional connectivity abnormalities in schizophrenia.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38698DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T15:21:11Z<p>Kubicki: /* Schedule */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - First clinical application of stochastic tractography module.<br />
* '''01/2009''' - First draft of the clinical paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline. <br />
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38697DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T15:16:24Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
:* Results presentation and paper submission. First step of data analysis has been already accomplished, we are in the process of registering structural and DTI data and preparing DTI data for stochastic tractography analysis. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2009). <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - Arcuate Fasciculus results.<br />
* '''01/2009''' - Arcuate Fasciuclus first draft of the paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38696DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T15:13:12Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was accepted and results presented at the World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Extracting paths of interest, and calculating FA along the paths for group comparison. <br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - Arcuate Fasciculus results.<br />
* '''01/2009''' - Arcuate Fasciuclus first draft of the paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38695DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T15:10:25Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was submitted and accepted for presentation at World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>June 10th 2009, paper accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - Arcuate Fasciculus results.<br />
* '''01/2009''' - Arcuate Fasciuclus first draft of the paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38694DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T15:09:33Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was submitted and accepted for presentation at World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>Paper was accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - Arcuate Fasciculus results.<br />
* '''01/2009''' - Arcuate Fasciuclus first draft of the paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38693DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T15:08:42Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was submitted and accepted for presentation at World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>Paper was accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Anatomical Connectivity Abnormalities in VCFS <br />
We use combination of structural MRI and DTI to investigate anatomical abnormalities that would characterize patients with VCFS, and relationship of these abnormalities to those observed in schizophrenia.<br />
:Project involves:<br />
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.<br />
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).<br />
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.<br />
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data. <br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - Arcuate Fasciculus results.<br />
* '''01/2009''' - Arcuate Fasciuclus first draft of the paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38692DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T14:56:54Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was submitted and accepted for presentation at World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''</font>Paper was accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - Arcuate Fasciculus results.<br />
* '''01/2009''' - Arcuate Fasciuclus first draft of the paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38691DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T14:55:26Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was submitted and accepted for presentation at World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* <font color="red">'''New: '''Paper was accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - Arcuate Fasciculus results.<br />
* '''01/2009''' - Arcuate Fasciuclus first draft of the paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=DBP2:Harvard:Brain_Segmentation_Roadmap&diff=38690DBP2:Harvard:Brain Segmentation Roadmap2009-06-15T14:54:33Z<p>Kubicki: /* Work in Progress */</p>
<hr />
<div>Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]<br />
__NOTOC__<br />
=Stochastic Tractography for VCFS=<br />
== Roadmap ==<br />
<br />
The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.<br />
<br />
== Algorithm ==<br />
<br />
[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]<br />
; A-Description <br />
* Most tractography methods estimate fibers by tracing the maximum direction of diffusion. A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. To address this uncertainty, stochastic tractography methods have been developed to quantify the uncertainty associated with estimated fibers (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. A comparison of the algorithms can be seen here. (Figure 1)<br />
<br />
[[Image:StochasticPic.PNG|thumb|right|200px|<font size=1>Figure 2: Stochastic tractography of uncinate fasciculis on anatomical data (left) and cingulum bungle on fMRI scan (right)</font>]]<br />
; B-Possible Applications <br />
* Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. Here is an example of this application to anatomical data (Figure 2, left image) and to fMRI data (Figure 2, right image). <br />
<br />
* Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). Example of such application to internal capsule. (Figure 3) <br />
<br />
[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]<br />
; C-References <br />
<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Björnemo M, Brun A, Kikinis R, Westin CF. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02). Tokyo, Japan, 2002;435-442.]<br />
* [http://lmi.bwh.harvard.edu/papers/pdfs/2006/frimanTMI06.pdf Friman, O., Farneback, G., Westin CF. A Bayesian Approach for Stochastic White Matter Tractography. IEEE Transactions on Medical Imaging, Vol 25, No. 8, Aug. 2006]<br />
<br />
[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]<br />
<br />
==Module== <br />
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY<br />
;Functionality of Python Stochastic Tractography module in Slicer 3.0<br />
* IO: <br />
Module reads files (DWI and ROIs) in nhdr format.<br />
* Smoothing:<br />
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.<br />
* Brain Mask:<br />
The Brain mask defines the volume in which the tensor will be computed and the tracts evaluated.<br />
*Diffusion Tensor:<br />
This step calculates the tensor and can output anisotropy indices (FA/Mode/Trace)<br />
*Tractography: <br />
Parameters that need to be adjusted:<br />
:1. The amount of tracts that will be seeded from each voxel (we recommend between 500 and 1000 tracts, depending on the workstation power- 1000 tracts per voxel seeded within the large ROI for high resolution DWI can take a long time to compute).<br />
:2. Maximum tract length (in mm), this can eliminate long, unwanted tracts if the regions for which connection is measured are located close to each other<br />
:3. Step Size: distance between each re-estimation of tensors, usually between 0.5 and 1 mm<br />
:4. Stopping criteria. This can be used on the top of WM mask, to terminate tracts (in case they really want to travel through the CSF, for example). <br />
*Connectivity Map:<br />
This step creates output probability maps. <br />
:1. binary: each voxel is counted only once if at least one fiber pass through it<br />
:2. cumulative: tracts are summed by voxel independently <br />
:3. weighted: tracts are summed by voxel depending on their length<br />
*Length Based:<br />
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.<br />
*Vicinity <br />
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.<br />
*Threshold <br />
This step will reject tracts whose endpoints are lower than the threshold value).<br />
*Spherical ROI vicinity <br />
This will make the ROI a sphere based on the ROI’s center of gravity (with the sphere’s radius being the distance from the center to the ROI’s furthest point). This sphere can be inflated by raising the Vicinity level to the number of steps you’d like to increase the ROI’s size by.<br />
<br />
<br />
<br />
Then, probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:Stochastic_June09_1.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
==Work Accomplished==<br />
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]<br />
; A - Optimization and testing of stochastic tractography algorithm :<br />
:* Original methodological paper, as well as our first attempts to use the algorithm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data ([https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&CPq_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_morph&up=CPq&JavaScript=enabled Structural MRI] and [https://portal.nbirn.net:443/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=gotoDirectory&up=7li&7li_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FHarvard%2Farchive_diffusion&JavaScript=enabled DTI data]). <br />
:* Tri worked hard on making sure algorithm works on new high resolution 3T data (available here: [https://portal.nbirn.net/gridsphere/gridsphere?cid=srbfilebrowser&gs_action=moveUpDir&gv7_dirpath=%2Fhome%2FProjects%2FNAMIC__0003%2FFiles%2FPNL%2F3T_strct_dti_fmri%2Fcase01017&up=gv7&JavaScript=enabled|PNL 3T Data]).<br />
:* Tests have been done also on the spiral diffusion phantom, to make sure diffusion directions and scanner coordinates are handled properly by the algorithm (Figure 6).<br />
<br />
; B - Clinical Applications <br />
<br />
:* Algorithm was used to trace and analyze anterior limb of the internal capsule on 1.5T data. It generated reacher representation of frontal fiber projections, it also turned out to be more sensitive to group differences in white matter integrity that conventional deterministic tractography (see Figure 6). <br />
:* Algorithm was also used to trace smaller white matter fiber tracts, such as Cingulum, Fornix, Uncinate Fasciculus, Arcuate Fasciculus on 3T "Santa Fe" dataset (http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference) <br />
<br />
; C - References<br />
<br />
:* [http://www.na-mic.org/Wiki/index.php/Image:IC_posternew.png Shenton, M.E., Ngo, T., Rosenberger, G., Westin, C.F., Levitt, J.J., McCarley, R.W., Kubicki, M. Study of Thalamo-Cortical White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Poster presented at the 46th Meeting of the American College of Neuropsychopharmacology, Boca Raton, FL, December 2007.]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:dougt_poster.pdf Terry DP, Rausch AC, Alvarado JL, Melonakos ED, Markant D, Westin CF, Kikinis R, von Siebenthal J, Shenton ME, Kubicki M. White Matter Properties of Emotion Related Connections in Schizophrenia. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* [http://www.na-mic.org/Wiki/index.php/Media:Jorge_poster.pdf Alvarado JL, Terry DP, Markant D, Ngo T, Kikinis R, Westin CF, McCarley RW, Shenton ME, Kubicki M. Study of Language-Related White Matter Tract Connections in Schizophrenia using Diffusion Stochastic Tractography. Poster presented at the 2009 Mysell Poster Day, Dept. of Psychiatry, Harvard Medical School, April 2009]<br />
:* Melonakos ED, Shenton ME, Markant D, Alvarado J, Westin CF, Kubicki M. White Matter Properties of Orbitofrontal Connections in Schizophrenia. Poster being presented at the 64th Meeting of the Society of Biological Psychiatry. Vancouver, BC. May 2009. <br />
:* Kubicki, M. Khan, U., Bobrow, L., O'Donnell, L. Pieper, S. Westin, CF., Shenton, ME. New Methods for Assessing Whole Brain DTI Abnormalities in Schizophrenia. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
:* Kubicki, M., Markant, D., Ngo, T., Westin, CF., McCarley, RW., Shenton, ME. Study of Language Related White Matter Fiber Tract Projections in Schizophrenia Using Diffusion Stochastic Tractography. Presentation given at the International Congress of World Psychiatric Association. Florence, Italy. April 2009.<br />
<br />
== Work in Progress ==<br />
<br />
; A - Optimization and Testing of stochastic tractography module :<br />
:* Julien is testing now separate components of work flow, including the masks, and impact of their precision on stochastic output, as well as impact of number of seeding points on tractography results.<br />
:* At the same time, we are testing the module on a Max Plank dataset of Anna Rotarska-Jagiela by looking at the connections through the corpus callosum of the left auditory cortex to the right auditory cortex as defined by fMRI activation maps as well as running this data for the tractography comparison project.<br />
:* We are discussing possibility of adding project specific functionality to module, such as third ROI to guide tractography, nonlinear registration button for merging fMRI and DTI data.<br />
<br />
; B - Related Clinical Projects <br />
<br />
;* Arcuate Fasciculus Extraction Project <br />
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]<br />
We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 7). This structure is especially important in both VCFS and schizophrenia, as it connects language related areas (Brocka and Wernicke's), and is involved in language processing quite disturbed in schizophrenia patients. It also can not be reliably traced using deterministic tractography. <br />
:Project involves:<br />
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri). <br />
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. <br />
:* Non-linear registration of labelmaps to the DTI space. <br />
:* Seeding tracts. We have piloted it using 5000 seeds per voxel, however it is quite time consuming running it on even most powerful computers in the lab, so we have experimented with smaller number of seeds per voxel. We tested 1000 seeds, which gave virtually identical results. <br />
:* Extracting path of interest, and calculating FA along the path for group comparison. Presentation of previous results for 7 schizophrenics and 12 control subjects, can be found here: [[Media:NAMIC_AHM_Arcuate.ppt|Progress Report Presentation]]. <br />
:* Results presentation and paper submission. Abstract was submitted and accepted for presentation at World Biological Psychiatry Symposium in Venice, Italy. Paper is in preparation.<br />
<br />
;* Semantic Network Connectivity Project<br />
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.<br />
:Project involves:<br />
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia<br />
:* Analysis of functional connectivity (using FSL) between nodes of semantic network<br />
:* Whole brain Voxel Based analysis of DTI data in same population<br />
:* Use of stochastic tractography to identify connections between functional nodes<br />
:* Correlational analysis involving anatomical and functional connectivity data.<br />
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been submitted to HBM.<br />
:* NEW: Paper was accepted for publication in Human Brain Mapping: "Functional and Anatomical Connectivity Abnormalities in Left Inferior Frontal Gyrus in Schizophrenia" by Jeong, Wible, Hashimoto and Kubicki, HBM in Press<br />
[[Image:Anna.png|thumb|right|200px|<font size=1>Figure 8: Max Plank data showing tracts through the corpus connecting 2 cortical ROIs defined by fMRI activations.</font>]]<br />
<br />
;* Study of Default Network<br />
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and has been put it into DTI space. Figure 8 shows pilot data of the white matter connections through the corpus callosum between the left and the right auditory cortex ROIs as defined by fMRI data. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips). <br />
<br />
;* Tractography Comparison Project<br />
We are also working on a [http://www.na-mic.org/Wiki/index.php/SanteFe.Tractography.Conference tractography comparison project]dataset, where we apply stochastic tractography to phantom, as well as test dataset. <br />
<br />
===Staffing Plan===<br />
<br />
* Sylvain and Yogesh are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs<br />
* Doug Markant, our NAMIC RA has left the lab, and now Doug Terry, is a new NAMIC RA. <br />
* Julien is our new NAMIC software engineer. He is responsible for improving the STM (Stochastic Tractography Module), and making sure software works with STM compliant datasets. <br />
[[Link Progress| Development Progress]] <br />
* Polina is the algorithm core contact<br />
* Brad is the engineering core contact<br />
<br />
<br />
===Schedule===<br />
<br />
* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data. <br />
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom. <br />
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data. <br />
* '''11/2008''' - Slicer 3 module prototype using python.<br />
* '''12/2008''' - Slicer 3 module official release <br />
* '''12/2008''' - Documentation and packaging for dissemination.<br />
* '''12/2008''' - Arcuate Fasciculus results.<br />
* '''01/2009''' - Arcuate Fasciuclus first draft of the paper.<br />
* '''05/2009''' - Distortion correction and nonlinear registration added to the module<br />
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.<br />
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.<br />
<br />
===Team and Institute===<br />
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)<br />
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal<br />
*NA-MIC Engineering Contact: Brad Davis, Kitware<br />
*NA-MIC Algorithms Contact: Polina Gollard, MIT<br />
<br />
===Publications===<br />
<br />
''In print''<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Kubicki+AND+Westin+AND+DTI&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Clinical Applications]<br />
<br />
*[http://www.na-mic.org/pages/Special:Publications?text=Ngo+AND+Golland&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database - Algorithms Development]<br />
<br />
<br />
[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=2009_Summer_Project_Week&diff=381032009 Summer Project Week2009-06-04T14:32:07Z<p>Kubicki: /* Attendee List */</p>
<hr />
<div>Back to [[Project Events]], [[Events]]<br />
<br />
*'''Dates:''' June 22-26, 2009<br />
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].<br />
<br />
<br />
==Introduction to the FIRST JOINT PROJECT WEEK==<br />
<br />
We are pleased to announce the FIRST JOINT PROJECT WEEK of hands-on research and development activity for Image-Guided Therapy and Neuroscience applications. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. <br />
<br />
Active preparation will begin on''' Thursday, April 16th at 3pm ET''', with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects. <br />
<br />
The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.<br />
<br />
This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January. <br />
<br />
A summary of all past NA-MIC Project Events that this FIRST JOINT EVENT is based on is available [[Project_Events#Past|here]].<br />
<br />
== Agenda==<br />
* Monday <br />
** noon-1pm lunch <br />
**1pm: Welcome (Ron Kikinis)<br />
** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template]) <br />
** 3:30-5:30pm Start project work<br />
* Tuesday <br />
** 8:30am breakfast<br />
**9:30-10am: NA-MIC Kit Overview (Jim Miller)<br />
** 10-10:30am Slicer 3.4 Update (Steve Pieper)<br />
** 10:30-11am Slicer IGT and Imaging Kit Update Update (Noby Hata, Scott Hoge)<br />
** 11am-12:00pm Breakout Session: [[2009 Project Week Breakout Session: Slicer-Python]] (Demian W)<br />
** noon lunch<br />
** 2:30pm-5pm: [[2009 Project Week Data Clinic|Data Clinic]] (Ron Kikinis)<br />
** 5:30pm adjourn for day<br />
* Wednesday <br />
** 8:30am breakfast<br />
** 9am-12pm Breakout Session: [[2009 Project Week Breakout Session: ITK]] (Luis Ibanez)<br />
** noon lunch<br />
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: 3D+T Microscopy Cell Dataset Segmentation]] (Alex G.)<br />
** 5:30pm adjourn for day<br />
* Thursday<br />
** 8:30am breakfast<br />
** 9-11pm Tutorial Contest Presentations<br />
** noon lunch<br />
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: XNAT]] (Dan M.)<br />
** 5:30pm adjourn for day<br />
* Friday <br />
** 8:30am breakfast<br />
** 10am-noon: [[Events:TutorialContestJune2009|Tutorial Contest Winner Announcement]] and [[#Projects|Project Progress Updates]]<br />
*** Noon: Lunch boxes and adjourn by 1:30pm.<br />
***We need to empty room by 1:30. You are welcome to use wireless in Stata.<br />
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]<br />
***Next Project Week [[AHM_2010|in Utah, January 4-8, 2010]]<br />
<br />
== Projects ==<br />
<br />
The list of projects for this week will go here.<br />
=== Collaboration Projects ===<br />
#[[2009_Summer_Project_Week_Project_Segmentation_of_Muscoskeletal_Images]]<br />
#[[2009_Summer_Project_Week_4D_Imaging| 4D Imaging (Perfusion, Cardiac, etc.) ]] (Junichi, Dan Blezek?, Steve, Alex G?)<br />
#[[2009_Summer_Project_Week_Liver_Ablation_Slicer|Liver Ablation in Slicer (Haiying, Ziv, Noby)]]<br />
#[[2009_Summer_Project_Week_Slicer3_Brainlab_Introduction|SLicer3, BioImage Suite and Brainlab - Introduction to UCLA (Haiying, Xenios, Pratik, Nathan Hageman)]]<br />
#Adaptive Radiotherapy - Deformable registration and DICOMRT (Greg Sharp, Steve, Wendy)<br />
#Brain DTI Atlas? (Florin, Utah, UNC, GeorgiaTech)<br />
#Slicer module for the computation of fibre dispersion and curving measures (Peter Savadjiev, C-F Westin)<br />
#Xnat user interface improvements for NA-MIC (Dan M, Florin, Ron, Wendy)<br />
#xnat and DICOMRT (Greg Sharp, Dan M) - might be done?<br />
#Grid Wizard+xnat clinic (Clement Vachet)<br />
#?Fluid Mechanincs Module (Nathan Hageman)<br />
#?DTI digital phantom generator to create validation data sets - webservice/cmdlin module/binaries are downloadable from UCLA (Nathan Hageman)<br />
#Cortical Thickness Pipeline (Clement Vachet, Ipek Oguz)<br />
#[[2009_Summer_Project_Week_Slicer3_Brainlab_Demo|Demo Brainlab-BioImage Suite-Slicer in BWH OR (Haiying, Isaiah, Nathan Hageman)]]<br />
#[[2009_Summer_Project_Week_Skull_Stripping | Skull Stripping]] (Xiaodong, Snehashis Roy)<br />
#[[2009_Summer_Project_Week_HAMMER_Registration | HAMMER Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller)<br />
#[[2009_Summer_Project_Week_WML_SEgmentation |White Matter Lesion segmentation]] (Minjeong Kim, Xiaodong Tao, Jim Miller)<br />
#[[2009_Summer_Project_Week-FastMarching_for_brain_tumor_segmentation |FastMarching for brain tumor segmentation]] (Fedorov, GeorgiaTech)<br />
#[[2009_Summer_Project_Week_Meningioma_growth_simulation|Meningioma growth simulation]] (Fedorov, Marcel, Ron)<br />
#Automatic brain MRI processing pipeline (Marcel, Hans)<br />
#XNAT integration into Harvard Catalyst i2b2 framework(Gao, Yong)<br />
#[[2009_Summer_Project_Week_Spherical_Mesh_Diffeomorphic_Demons_Registration |Spherical Mesh Diffeomorphic Demons Registration]] (Luis Ibanez,Thomas Yeo, Polina Goland), - (Mon, Tue, Wed)<br />
#[[2009_Summer_Project_Week_MRSI-Module|MRSI Module]] (Bjoern Menze, Jeff Yager, Vince Magnotta)<br />
#[[Measuring Alcohol Stress Interaction]] (Vidya Rajgopalan, Andrey Fedorov)<br />
#DWI/DTI QC and Preparation Tool: DTIPrep (Zhexing Liu)<br />
<br />
===IGT Projects:===<br />
#[[2009_Summer_Project_Week_Prostate_Robotics |Prostate Robotics]] (Junichi, Sam, Nathan Cho, Jack), - Mon, Tue, Thursday 7pm-midnight)<br />
#port 4d gated ultrasound code to Slicer - (Danielle)<br />
#integration of stereo video into Slicer (Mehdi)<br />
#multi-modality statistical toolbox for MR T1, T2, fMRI, DTI data (Diego, sylvain jaume, nicholas, noby)<br />
#neuroendoscope workflow presentation (sebastien barre)<br />
#breakout session on Dynamic Patient Models (James Balter)<br />
#[[2009_Summer_Project_Week_Registration_for_RT|2d/3d Registration (and GPGPU acceleration) for Radiation Therapy]] (Sandy Wells, Jim Balter, and others)<br />
<br />
===NA-MIC Engineering Projects===<br />
# DICOM Validation and Cleanup Tool (Luis, Sid, Steve, Greg)<br />
# [[Summer2009:Using_ITK_in_python| Using ITK in python]] (Steve, Demian, Jim)<br />
# [[Summer2009:Implementing_parallelism_in_python| Taking advantage of multicore machines & clusters with python]] (Julien de Siebenthal, Sylvain Bouix)<br />
# [[Summer2009:Using_client_server_paradigm_with_python_and_slicer| Deferring heavy computational tasks with python]] (Julien de Siebenthal, Sylvain Bouix)<br />
# [[Summer2009:Using_CUDA_for_stochastic_tractography| Developing realtime feedback using CUDA]] (Julien de Siebenthal, Sylvain Bouix)<br />
# [[2009_Summer_Project_Week_VTK_3D_Widgets_In_Slicer3|VTK 3d Widgets in Slicer3]] (Nicole, Karthik, Sebastien, Wendy)<br />
# [[2009_Summer_Project_Week_Colors_Module |Updates to Slicer3 Colors module]] (Nicole)<br />
# [[EM_Segmenter|EM Segmenter]] (Sylvain Jaume, Nicolas Rannou)<br />
# Plug-in 3D Viewer based on XIP (Lining)<br />
# [[MeshingSummer2009 | IAFE Mesh Modules - improvements and testing]] (Curt, Steve, Vince)<br />
# [[Slicer3 Informatics Workflow Design & XNAT updates | Slicer3 Informatics Workflow Design & XNAT updates for Slicer]] (Wen, Steve, Dan M, Dan B)<br />
# [[BSpline Registration in Slicer3 | BSpline Registration in Slicer3]] (Samuel Gerber,Jim Miller, Ross Whitaker)<br />
# [[EPI Correction in Slicer3 | EPI Correction in Slicer3]] (Ran Tao, Jim Miller, Sylvain Bouix, Tom Fletcher, Ross Whitaker, Julien de Siebenthal)<br />
# [[Summer2009:Registration reproducibility in Slicer|Registration reproducibility in Slicer3]] (Andriy, Luis, Bill, Jim, Steve)<br />
# [[Summer2009:The Vascular Modeling Toolkit in 3D Slicer | The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn)<br />
<br />
== Preparation ==<br />
<br />
# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list<br />
# Join the kickoff TCON on April 16, 3pm ET.<br />
# [[Engineering:TCON_2009|June 18 TCON]] at 3pm ET to tie loose ends. Anyone with un-addressed questions should call.<br />
# By 3pm ET on June 11, 2009: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.<br />
# By 3pm on June 18, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)<br />
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)<br />
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)<br />
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)<br />
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...<br />
# People doing Slicer related projects should come to project week with slicer built on your laptop.<br />
## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-4/#dirlist Slicer-3-4 branch] (new code should not be checked into the branch).<br />
## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].<br />
<br />
==Attendee List==<br />
If you plan to attend, please add your name here.<br />
<br />
#Ron Kikinis, BWH (NA-MIC, NAC, NCIGT)<br />
#Ferenc Jolesz, BWH (NCIGT, NAC)<br />
#Clare Tempany, BWH (NCIGT)<br />
#Tina Kapur, BWH (NA-MIC, NCIGT)<br />
#Steve Pieper, Isomics Inc<br />
#Jim Miller, GE Research<br />
#Xiaodong Tao, GE Research<br />
#Randy Gollub, MGH<br />
#Nicole Aucoin, BWH (NA-MIC)<br />
#Dan Marcus, WUSTL<br />
#Junichi Tokuda, BWH (NCIGT)<br />
#Alex Gouaillard, Harvard Systems Biology<br />
#Arnaud Gelas, Harvard Systems Biology <br />
#Kishore Mosanliganti, Harvard Systems Biology<br />
#Lydie Souhait, Harvard Systems Biology<br />
#Luis Ibanez, Kitware Inc<br />
#Vincent Magnotta, UIowa<br />
#Hans Johnson, UIowa<br />
#Xenios Papademetris, Yale<br />
#Gregory S. Fischer, WPI (Mon, Tue, Wed)<br />
#Daniel Blezek, Mayo (Tue-Fri)<br />
#Danielle Pace, Robarts Research Institute / UWO<br />
#Clement Vachet, UNC-Chapel Hill<br />
#Dave Welch, UIowa<br />
#Demian Wassermann, Odyssée lab, INRIA, France<br />
#Manasi Ramachandran, UIowa<br />
#Greg Sharp, MGH<br />
#Rui Li, MGH<br />
#Mehdi Esteghamatian, Robarts Research Institute / UWO<br />
#Misha Milchenko, WUSTL<br />
#Kevin Archie, WUSTL<br />
#Tim Olsen, WUSTL<br />
#Wendy Plesniak BWH (NAC)<br />
#Haiying Liu BWH (NCIGT)<br />
#Curtis Lisle, KnowledgeVis / Isomics<br />
#Diego Cantor, Robarts Research Institute / UWO<br />
#Daniel Haehn, BWH<br />
#Nicolas Rannou, BWH<br />
#Sylvain Jaume, MIT<br />
#Alex Yarmarkovich, Isomics<br />
#Marco Ruiz, UCSD<br />
#Andriy Fedorov, BWH (NA-MIC)<br />
#Harish Doddi, Stanford University<br />
#Saikat Pal, Stanford University<br />
#Scott Hoge, BWH (NCIGT)<br />
#Vandana Mohan, Georgia Tech<br />
#Ivan Kolosev, Georgia Tech<br />
#Behnood Gholami, Georgia Tech<br />
#James Balter, U Michigan<br />
#Dan McShan, U Michigan<br />
#Zhou Shen, U Michigan<br />
#Maria Francesca Spadea, Italy<br />
#Lining Yang, Siemens Corporate Research<br />
#Beatriz Paniagua, UNC-Chapel Hill<br />
#Bennett Landman, Johns Hopkins University <br />
#Snehashis Roy, Johns Hopkins University<br />
#Marta Peroni, Politecnico di Milano<br />
#Sebastien Barre, Kitware, Inc.<br />
#Samuel Gerber, SCI University of Utah<br />
#Ran Tao, SCI University of Utah<br />
#Marcel Prastawa, SCI University of Utah<br />
#Katie Hayes, BWH (NA-MIC)<br />
#Sonia Pujol, BWH (NA-MIC)<br />
#Andras Lasso, Queen's University<br />
#Yong Gao, MGH<br />
#Minjeong Kim, UNC-Chapel Hill<br />
#Guorong Wu, UNC-Chapel Hill<br />
#Jeffrey Yager, UIowa<br />
#Yanling Liu, SAIC/NCI-Frederick<br />
#Ziv Yaniv, Georgetown<br />
#Bjoern Menze, MIT<br />
#Vidya Rajagopalan, Virginia Tech<br />
#Sandy Wells, BWH (NAC, NCIGT)<br />
#Lilla Zollei, MGH (NAC)<br />
#Lauren O'Donnell, BWH<br />
#Florin Talos, BWH (NAC)<br />
#Nobuhiko Hata, BWH (NCIGT)<br />
#Alark Joshi, Yale<br />
#Yogesh Rathi, BWH<br />
#Jimi Malcolm, BWH<br />
#Dustin Scheinost, Yale<br />
#Dominique Belhachemi, Yale<br />
#Sam Song, JHU<br />
#Nathan Cho, JHU<br />
#Julien de Siebenthal, BWH<br />
#Peter Savadjiev, BWH<br />
#Carl-Fredrik Westin, BWH<br />
#John Melonakos, AccelerEyes (Wed & Thu morning)<br />
#Yi Gao, Georgia Tech<br />
#Sylvain Bouix, BWH<br />
#Zhexing Liu, UNC-CH<br />
#Eric Melonakos, BWH<br />
#Lei Qin, BWH<br />
#Giovanna Danagoulian, BWH<br />
#Andrew Rausch, BWH (1st day only)<br />
#Haytham Elhawary, BWH<br />
#Jayender Jagadeesan, BWH<br />
#Marek Kubicki, BWH<br />
<br />
== Logistics ==<br />
*'''Dates:''' June 22-26, 2009<br />
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].<br />
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). Due by Friday, June 12th, 2009. Please make checks out to "Massachusetts Institute of Technology" and mail to: Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409a, Cambridge, MA 02139. Receipts will be provided by email as checks are received. Please send questions to dkauf at mit.edu. '''If this is your first event and you are attending for only one day, the registration fee is waived.''' Please let us know, so that we can cover the costs with one of our grants.<br />
*'''Registration Method''' Add your name to the Attendee List section of this page<br />
*'''Hotel:''' We have a group rate of $189/night (plus tax) at the Le Meridien (which used to be the Hotel at MIT). [http://www.starwoodmeeting.com/Book/MITDECSE Please click here to reserve.] This rate is good only through June 1.<br />
*Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.<br />
*2009 Summer Project Week [[NA-MIC/Projects/Theme/Template|'''Template''']]<br />
*[[2008_Summer_Project_Week#Projects|Last Year's Projects as a reference]]<br />
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]</div>Kubickihttps://www.na-mic.org/w/index.php?title=2009_Annual_Scientific_Report&diff=362932009 Annual Scientific Report2009-04-15T17:57:34Z<p>Kubicki: /* Key Investigators */</p>
<hr />
<div>Back to [[2009_Progress_Report]]<br />
<br />
<br />
<br />
<br />
<br />
=Guidelines for preparation=<br />
<br />
*[[2009_Progress_Report#Scientific Report Timeline]] - Main point is that May 15 is the date by which all sections below need to be completed. No extensions are possible.<br />
*DBPs - If there is work outside of the roadmap projects that you would like to report, you are welcome to create a separate section for it under "Other". <br />
*The outline for this report is similar to the 2008 and 2007 reports, which are provided here for reference: [[2008_Annual_Scientific_Report]], [[2007_Annual_Scientific_Report]].<br />
*In preparing summaries for each of the 8 topics in this report, please leverage the detailed pages for projects provided here: [[NA-MIC_Internal_Collaborations]].<br />
*Publications will be mined from the SPL publications database. All core PIs need to ensure that all NA-MIC publications are in the publications database by May 15.<br />
<br />
=Introduction (Tannenbaum)=<br />
<br />
The National Alliance for Medical Imaging Computing (NA-MIC) is now in its fifth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. This was our second year with our current DBPS of which three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. The and fourth is a very new direction, the prostate: brachytherapy needle positioning robot integration.<br />
<br />
We briefly summarize the work of NAMIC during the five years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main themes: Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. The third year of center activity was devoted to the continuation of the collaborative efforts in order to give solutions to the various brain-oriented DBPs. The fourth year was focused on translating our work to the new DBPs. In the fifth year, a number of projects reached the point where modules were introduced<br />
into Slicer making the Core 1 algorithms available to the general medical imaging community. A number of the algorithms are quite general and can be used for purposes much broader than the original DBPs. For example, a new point cloud registration algorithm was developed for the prostate brachytherapy needle positioning project can be used also for DWI registration.<br />
Work on DTI/DWI tractography has impacted the segmentation of blood vessels and soft plaque detection in the coronaries.<br />
<br />
Year five has seen progress with the work of our current DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.<br />
<br />
In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent work on the four infrastructure topics. These include: Diffusion Image analysis (Section 4.1), Structural analysis (Section 4.2), Functional MRI analysis (Section 4.3), and the NA-MIC Toolkit (Section 4.4). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final sections of this report, Sections 5-11, provide updated timelines on the status of the various projects of the different cores of NAMIC.<br />
<br />
=Clinical Roadmap Projects=<br />
==Roadmap Project: Stochastic Tractography for VCFS (Kubicki)==<br />
===Overview (Kubicki)===<br />
The goal of this project is to create an end-to-end application that would be useful in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-facial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.<br />
<br />
===Algorithm Component (Golland)===<br />
At the core of this project is the stochastic tractography algorithm<br />
developed and implemented in collaboration between MIT and<br />
BWH. Stochastic Tractography is a Bayesian approach to estimating<br />
nerve fiber tracts from DTI images.<br />
<br />
We first use the diffusion tensor at each voxel in the volume to<br />
construct a local probability distribution for the fiber direction<br />
around the principal direction of diffusion. We then sample the tracts<br />
between two user-selected ROIs, by simulating a random walk between<br />
the regions, based the local transition probabilities inferred from<br />
the DTI image.<br />
<br />
The resulting collection of fibers and the associated FA values<br />
provide useful statistics on the properties of connections between the<br />
two regions. To constrain the sampling process to the relevant white<br />
matter region, we use atlas-based segmentation to label ventricles and<br />
gray matter and to exclude them from the search space. As such, this<br />
step relies heavily on the registration and segmentation functionality<br />
in Slicer.<br />
<br />
Over the last year, we have been working on applying several pre- and postprocessing steps to the algorithm pipeline. These steps include eddy current and geometric distortion correction that have been made available to us by Utah group, as well as DTI filtering (BWH). White matter masks can also now be created based on T2 thresholding within the slicer stochastic tractography module, which makes them more precise, since they do not rely on MRI to DTI co-registration. <br />
<br />
At the same time we are working on the datasets where fMRI activations as well as gray matter segmentations need to be registered to DTI data, in order to seed within the predefined gray matter regions. We have made a significant progress in between modality registration, additional improvement is expected when distortion correction become part of the analysis pipeline. <br />
<br />
We are also working on improved ways to visualize and quantify stochastic tractography output, not only by parametrizing fiber tracts, but also by creating connection probability distribution maps.<br />
<br />
===Engineering Component (Davis)===<br />
Stochastic Tractography slicer module has been rewritten in python now, and new module released in December 2008, and presented at the AHM in SLC. Its now part of the slicer3. Module documentation have been also created. Current engineering efforts are concentrated on maintaining the module, optimizing it for working with other data formats, and adding new functionality, such as better registration, distortion correction and ways of extracting and measuring FA along the tracts. <br />
<br />
Also, because of the fact that the new data is much more computationally demanding (higher spatial resolution, more diffusion directions), and cortical ROIs usually much larger than the previously used WM ROIs, there is general need for performance improvement. This issue is highlighted especially by our stochastic way of tracking connections, where hundreds, instead of just one, (as in deterministic tractography) tracts are being generated from one seed. Thus some of our efforts go towards multithreading, and utilizing parallel processing. Version of our algorithm that uses large computer clusters have been developed and can be downloaded and installed by individual users with minimal knowledge of parallel computing now.<br />
<br />
===Clinical Component (Kubicki)===<br />
Over the last year, we tested the algorithm on newly released 3T NAMIC data, which contains high resolution DTI as well as structural RM data, plus automatic anatomical segmentations. Data is already co-registered, so cortical ROIs can be used as seeding points for stochastic tractography. <br />
<br />
Using this dataset, we have completed a clinical study, where we looked at the connections between inferior frontal and superior temporal lobes, sites of the language network. Connections of these two regions, obtained with stochastic tractography, have been measured, and compared between group of 20 chronic schizophrenia patients and 20 controls. We have also looked at gray matter volumes of destination regions, trying to estimate relationship between gray and white matter abnormalities in schizophrenia. Results of this study have been presented at World Psychiatry Congress in Florence, Italy in April of 2009, as well as at Harvard Psychiatry MYSELL conference also in April 2009. <br />
<br />
Another clinical study that is under way, is the application of stochastic tractography to define connections involved in emotional processing. For this purpose, we use cortical segmentations of anterior cingulated gyrus, orbital-frontal gyrus and amygdala, and trace as well as quantify connections between there regions in healthy controls as well in schizophrenia patients. Results of this preliminary study have been presented at MYSELL in April 2009, and will be presented at Biological Psychiatry conference later this year. <br />
<br />
We are also involved in two collaborative studies. In one, use DTI data acquired in at UCI, and apply stochastic method to segment and measure arcuate fasciculus in subjects with schizophrenia and language impairment, as evinced in ERP data. In another collaboration, we combine resting state fMRI data with DTI in order to measure connectivity between regions forming functional network. Both these projects are under way. <br />
<br />
Finally, stochastic tractography have been used qualitatively in one publication that is in press in Human Brain Mapping. Here, we combined fMRI with DTI whole brain data analysis, and found regions that were expressing abnormal functional connectivity in schizophrenia. These regions were then assigned to certain anatomical structures (white mater tracts), based on their location, and relationship to stochastic tractography output.<br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard:Brain_Segmentation_Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==<br />
===Overview (Fichtinger)===<br />
Numerous studies have demonstrated the efficacy of image-guided<br />
needle-based therapy and biopsy in the management of prostate<br />
cancer. The accuracy of traditional prostate interventions performed using<br />
transrectal ultrasound (TRUS) is limited by image fidelity, needle<br />
template guides, needle deflection and tissue deformation. Magnetic Resonance<br />
Imaging (MRI) is an ideal modality for guiding and monitoring<br />
such interventions due to its excellent visualization of the prostate, its<br />
sub-structure and surrounding tissues. <br />
<br />
We have designed a comprehensive robotic assistant system that allows prostate biopsy and brachytherapy procedures to be performed entirely inside a 3T closed MRI scanner. The current system applies transrectal approach to the prostate: an endorectal coil and steerable needle guide, both tuned to 3T magnets and invariable to any particular scanner, are integrated into the MRI compatible manipulator.<br />
<br />
Under the NAMIC initiative, the image computing, visualization, intervention planning, and kinematic planning interface is being accomplished with open source system built on the NAMIC toolkit and its components, such as Slicer3 and ITK. These are complemented by a collection of unsupervised prostate segmentation and registration methods that are of great importance to the clinical performance of the interventional system as a whole.<br />
<br />
===Algorithm Component (Tannenbaum)===<br />
<br />
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps below.<br />
<br />
'''Prostate Segmentation'''<br />
<br />
We must first extract the prostate. We provided two methods: a shape based method and a semi-automatic method. More details are given below and images and further details may be found [http://www.na-mic.org/Wiki/index.php/Projects:ProstateSegmentation here]<br />
<br />
# ''A shape based algorithm''. This begins with learning a group of shapes, obtained from manually segmenting a set of prostate 3D images. With the shapes represented as the hyperbolic tangent of the signed distance functions, principle component analysis is employed to learn the shapes. Further, given a new prostate image, we search the learned shape space in order to find one shape best segment the given image. The fitness of one shape to segment the image is evaluated by an energy functional measuring the discrepancy of the statistical characteristics inside and outside the current segmentation boundary. Such method is robust to the noise in the images. Moreover, the whole algorithm pipeline has been integrated into the Slicer3 through the command line module.<br />
# ''Semi-automatic method''. This method is based on a random walk segmentation algorithm. With user provided initial seed regions inside and out side the object (prostate), the algorithm computes a probability distribution over the image domain by solving a boundary value partial differential equation where the value at seed regions are fixed at 1.0 or 0.0, depending or whether they are object or background seeds. The resulting distribution indicates the probability of each voxel belonging to the object. Simply threshold by 0.5 gives the segmentation of the object. Moreover, if the result is not suitable, the user can edit the seed regions, and the new result is computed based on this previous result. This algorithm has been integrated into the transrectal prostate MRI module of Slier3.<br />
<br />
'''Prostate Registration'''<br />
<br />
We developed a nonlinear (affine) prostate registration method by treating prostate images as point sets. Then the iterative closest point algorithm is improved to register the point sets generated by the two images to be registered. The proposed method shows robustness to long distance transition and partial image structure. Moreover, such representation is much sparser than sampling image on the uniform grid thus the registration is very fast comparing two 3D volumetric<br />
image registration.<br />
<br />
Furthermore, the registration is viewed as a posterior estimation problem, in which the distributions of the affine and translation parameters are to be estimated. This can naturally be estimated using a particle filter framework. Through this, the method can handle the otherwise difficult cases where the two prostates are one supine and<br />
one prone.<br />
<br />
More details are given [[Projects:pfPtSetImgReg|here...]]<br />
<br />
===Engineering Component (Hayes)===<br />
<Note Progress in the last year><br />
<br />
<br />
===Clinical Component (Fichtinger)===<br />
<Note Progress in the last year><br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:JHU:Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus (Bockholt)==<br />
===Overview (Bockholt)===<br />
The primary goal of the MIND DPB is to examine changes in white matter lesions in adults with Neuropsychiatric Systemic Lupus Erythematosus (SLE). We want to be able to characterize lesion location, size, and intensity, and would also like to examine longitudinal changes of lesions in an SLE cohort. To accomplish this goal, we will create an end-to-end application entirely within NA-MIC Kit allowing individual analysis of white matter lesions. Such a workflow will then be applied to a clinical sample in the process of being collected.<br />
<br />
===Algorithm Component (Whitaker)===<br />
The basic steps necessary for the white matter lesion analysis application entail first registration of T1, T2, and FLAIR images, second tissue classification into gray, white, csf, or lesion, thirdly clustering lesion for anatomical localization, and finally a summarization of lesion size and image intensity parameters within each unique lesion. <br />
<br />
<Note Progress in the last year><br />
<br />
===Engineering Component (Pieper)===<br />
<Note Progress in the last year><br />
<br />
===Clinical Component (Bockholt)===<br />
<Note Progress in the last year><br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND:Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Cortical Thickness for Autism(Hazlett)== <br />
===Overview (Hazlett)===<br />
<br />
A primary goal of the UNC DPB is to examine changes in cortical thicknes in children with autism compared to typical controls. We want to examine group differences in both local and regional cortical thickness, and would also like to examine longitudinal changes in the cortex from ages 2-4 years. To accomplish this goal, this project will create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness. Such a workflow will then be applied to our study data (already collected).<br />
<br />
===Algorithm Component (Styner)===<br />
<br />
The basic steps necessary for the cortical thickness application entail first tissue segmentation in order to separate white and gray matter regions, second cortical thickness measurement, thirdly cortical correspondence to compare measurements across subjects and finally a statistical analysis to locally compute group differences.<br />
<br />
<Note Progress in the last year><br />
<br />
===Engineering Component (Miller, Vachet)===<br />
<br />
<Note Progress in the last year><br />
<br />
===Clinical Component (Hazlett)===<br />
<Note Progress in the last year><br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNC:Cortical_Thickness_Roadmap here on the NA-MIC wiki].<br />
<br />
=Four Infrastructure Topics=<br />
==Diffusion Image Analysis (Gerig)==<br />
<Note Progress in the last year><br />
===Key Investigators===<br />
<br />
<Need to update the list below><br />
<br />
* BWH: Marek Kubicki, Martha Shenton, Sylvain Bouix, Julien von Siebenthal, Thomas Whitford, Jennifer Fitzsimmons, Doug Terry, Jorge Alverado, Eric Melonakos, Carl-Fredrik Westin.<br />
* MIT: Lauren O'Donnell, Polina Golland<br />
* UCI: James Fallon, Judi Ford<br />
* Utah I: Tom Fletcher, Ross Whitaker, Ran Tao, Yongsheng Pan<br />
* Utah II: Casey Goodlett, Sylvain Gouttard, Guido Gerig<br />
* GA Tech: John Melonakos, Vandana Mohan, Shawn Lankton, Allen Tannenbaum<br />
* GE: Xiaodong Tao, Jim Miller, Mahnaz Mandah<br />
* Isomics: Steve Pieper<br />
* Kitware: Luis Ibanez<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:DiffusionImageAnalysis here on the NA-MIC wiki].<br />
<br />
==Structural Analysis(Tannenbaum)==<br />
===Progress===<br />
Under Structural Analysis, the main topics of research for NAMIC are structural segmentation, registration techniques and shape analysis. These topics are correlated and hence research in one often finds application in another. For example, shape analysis can yield useful priors for segmentation, or segmentation and registration can provide structural correspondences for use in shape analysis and so on. <br />
<br />
An overview of selected progress highlights under these broad topics follows:<br />
<br />
Segmentation<br />
<br />
* Geodesic Tractography Segmentation: We proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. This has been applied successfully to the segmentation of neural fiber bundles such as the Cingulum Bundle. This framework has now been integrated into Slicer and is being tested on a population of brain data sets.<br />
<br />
* Tubular Surface Segmentation: We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels. <br />
<br />
* Local-global Segmentation: We have proposed a novel segmentation approach that combines the advantages of local and global approaches to segmentation, by using statistics over regions that are local to each point on the evolving countour. This makes it well suited to applications with contrast differences within the structure of interest such as in blood vessel segmentation, as well as applications like the neural fiber bundles where the diffusion profiles of voxels within the structure are locally similar but vary along the length of the fiber bundle itself.<br />
<br />
* Shape-based segmentation: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have presented an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. We have applied the algorithm successfully to real MRI images, and we have also implemented it into 3D Slicer.<br />
<br />
* Re-Orientation Approach for Segmentation of DW-MRI: This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation which allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares very favorably with segmentation by full-brain streamline tractography. <br />
<br />
<br />
Registration<br />
<br />
* Optimal Mass Transport based Registration: We have provided a computationaly efficient non-rigid/elastic image registration algorithm based on the Optimal Mass Transport theory. We use the Monge-Kantorovich formulation of the Optimal Mass Transport problem and implement the solution proposed by Haker et al. using multi-resolution and multigrid techniques to speed up the convergence. We also leverage the computation power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We extend the work by Haker et al. who compute the optimal warp from a first order partial differential equation, an improvement over earlier proposed higher order methods and those based on linear programming, and further implement the algorithm using a coarse-to-fine strategy resulting in phenomenol improvement in convergence. We have applied it successfully to the registration of 3D brain MRI datasets (preoperative and intra-operative), and are currently extending it to the non-rigid registration of baseline DWI to brain MRI data.<br />
<br />
* Atlas Regularization for Image Segmentation: Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. <br />
<br />
* Point-set Rigid Registration: We have proposed a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation. Moreover, we incorporate stochastic dynamics to model the uncertainity of the registration process. We treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temoral coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets.We applied the algorithm to different alignments of point clouds and it successfully found the correct optimal transformation that aligns two given point clouds despite the differing geometry around the local neighborhood of a point within their respective sets. <br />
<br />
* Regularization for Optimal Mass Transport: To extend the flexibility of the existing OMT algorithm, we added a regularization term to the functional being minimized. This term controls the tradeoff between how well two images match after registration versus how warped the transformation map can become. A weighted sum of squared differences is used to penalize having to move mass over long distances; this addition also helps to keep the transformation physically accurate by reducing the likelihood that the transformation grid will fold over itself and keeping the grid smooth.<br />
<br />
* Registration of DW-MRI to structural MRI: Optimal Mass Transport was applied to the problem of correcting EPI distortion in DW-MRI. A mask for white matter in DW-MRI was registered to the white matter mask extracted from the structural MRI for the same patient. Prior to registration, it is important to normalize intensities in the two masks; this was done by dividing the images into regions and uniformly normalizing over each region to assure the sum of the intensities is equal. Then, once a transformation between the white matter masks was calculated, this transformation was applied to the original DW-MRI image. <br />
<br />
Shape Analysis<br />
<br />
* Shape Analysis Framework using SPHARM-PDM: We have provided an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are first processed to fill any interior holes. The processed binary segmentations are converted to surface meshes, and a spherical parametrization is computed for the surface meshes using a area-preserving, distortion minimizing spherical mapping. The SPHARM description is computed from the mesh and its spherical parametrization. Using the first order ellipsoid from the spherical harmonic coefficients, the spherical parametrizations are aligned to establish correspondence across all surfaces. The SPHARM description is then sampled into a triangulated surfaces (SPHARM-PDM) via icosahedron subdivision of the spherical parametrization. These SPHARM-PDM surfaces are all spatially aligned using rigid Procrustes alignment. Group differences between groups of surfaces are computed using the standard robust Hotelling T 2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. We provide additional visualization of the group tests via mean difference magnitude and vector maps, as well as maps of the group covariance information. We have a stable implementation, and current development focuses on integrating the current command line tools into Slicer (v3) via the Slicer execution model. <br />
<br />
* Population studies using Tubular Surface Model: We have proposed a tubular shape model for the Cingulum Bundle which models a tubular surface as a center-line coupled with a radius function at every point along the center-line. This model shows potential for population studies on the Cingulum Bundle which is believed to be involved in Schizophrenia, since it provides a natural way of sampling the structure to build a feature representation of it. We are currently segmenting the Cingulum Bundle from a population of brain data sets, towards performing this population analysis using the Pott's Model.<br />
<br />
* Automatic Outlining of Sulci on a Brain Surface: We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain, where the data is taken to be a 3D triangulated mesh formed from the segmentation of MR image slices. The problem is posed as energy minimization using penalizing the arc-length of segmenting curve using conformal factor involving the mean curvature of the underlying surface. The computation is made practical for dense meshes via the use of a sparse-field method to track the level set interfaces and regularized least-squares estimation of geometric quantities.<br />
<br />
===Key Investigators===<br />
<br />
Needs to be updated:<br />
<br />
* MIT: Polina Golland, Kilian Pohl, Sandy Wells, Eric Grimson, Mert R. Sabuncu<br />
* UNC: Martin Styner, Ipek Oguz, Xavier Barbero <br />
* Utah: Ross Whitaker, Guido Gerig, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer <br />
* GaTech: Allen Tannenbaum, John Melonakos, Vandana Mohan, Tauseef ur Rehman, Shawn Lankton, Samuel Dambreville, Yi Gao, Romeil Sandhu, Xavier Le Faucheur, James Malcolm, Ivan Kolosev<br />
* Isomics: Steve Pieper <br />
* GE: Bill Lorensen, Jim Miller <br />
* Kitware: Luis Ibanez, Karthik Krishnan<br />
* UCLA: Arthur Toga, Michael J. Pan, Jagadeeswaran Rajendiran <br />
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt, Yogesh Rathi, Marek Kubicki, Steven Haker<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:StructuralImageAnalysis here on the NA-MIC wiki].<br />
==fMRI Analysis (Golland)==<br />
===Progress===<br />
One of the major goals in analysis of fMRI data is the detection of<br />
functionally homogeneous networks in the brain. <br />
<br />
<note progress here><br />
<br />
===Key Investigators===<br />
<br />
Need to update this list:<br />
<br />
# MIT: Polina Golland, Danial Lashkari, Bryce Kim <br />
# Harvard/BWH: Sylvain Bouix, Martha Shenton, Marek Kubicki<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:fMRIAnalysis here on the NA-MIC wiki].<br />
==NA-MIC Kit Theme (Schroeder)==<br />
===Progress===<br />
The NAMIC-Kit consists of a framework of advanced computational components, as well as the support infrastructure for testing, documenting, and deploying leading edge medical imaging algorithms and software tools. The framework has been carefully constructed to provide low-level access to libraries and modules for advanced users, plus high-level application access that non-computer professionals can use to address a variety of problems in biomedical computing. In this fifth year of the NA-MIC projects <summary of progress><br />
<br />
===Software Releases===<br />
The NAMIC-Kit can be represented as a pyramid of capabilities, with the base consisting of toolkits and libraries, and the apex standing in for the Slicer3 user application. In between, Slicer modules are stand-alone executables that can be integrated directly into the Slicer3 application, including GUI integration, while work-flows are groups of modules that are integrated together to manifest sophisticated segmentation, registration and biomedical computing algorithms. In a coordinated NAMIC effort, major releases of these many components were realized over the past year. This includes, but is not limited to:<br />
*<br />
*<br />
<br />
===Slicer3 and the Software Framework===<br />
One of the major achievements of the past year has been...<br />
<br />
===Software Process===<br />
One of the challenges facing developers has been the requirement to implement, test and deploy software systems across multiple computing platforms. NAMIC continues to push the state of the art with further development of the CMake, CTest, and CPack tools for cross-platform development, testing, and packaging, respectively...<br />
<br />
===Key Investigators===<br />
THis list needs to be updated:<br />
<br />
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman<br />
* GE - Jim Miller, Xiaodong Tao<br />
* Isomics - Steve Pieper<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC-Kit here on the NA-MIC wiki].<br />
<br />
<br />
<br />
=Highlights(Schroeder)=<br />
===Advanced Algorithms===<br />
<br />
===NAMIC-Kit===<br />
<br />
===Outreach and Technology Transfer===<br />
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.<br />
* The Slicer community held several workshops and tutorials. In xxx a satellite event was held for the international Organization for Human Brain Mapping at the annual meeting in xxx. The xx workshop on xx hosted xx participants representing xx countries from around the world, xx states within the US and xxdifferent laboratories including xx NIH institutes. In addition, <note how many slicer tutorials were held and where etc><br />
* Project Week continues to be a successful NAMIC venue. These semi-annual events are held in Boston in June, and January in Salt Lake City. These events are well attended with approximately 100 participants, of which about a third are outside collaborators. At the last Project Week in Salt Lake City, approximately xx projects were realized.<br />
* NAMIC continues to participate in conferences and other technical venues. For example, NAMIC hosted xxx<br />
<br />
<br />
=Impact and Value to Biocomputing (Miller)=<br />
NA-MIC impacts Biocomputing through a variety of mechanisms. First,<br />
NA-MIC produces scientific results, methodologies, workflows,<br />
algorithms, imaging platforms, and software engineering tools and<br />
paradigms in an open enviroment that contributes directly to the body of<br />
knowledge available to the field. Second, NA-MIC science and<br />
technology enables the entire medical imaging community to build on<br />
NA-MIC results, methods, and techniques, to concentrate on the new<br />
science instead of developing supporting infrastructure, to leverage<br />
NA-MIC scientists and engineers to adapt NA-MIC technology to new<br />
problem domains, and to leverage NA-MIC infrastructure to distribute<br />
their own technology to a larger community.<br />
<br />
===Impact within the Center===<br />
<br />
===Impact within NIH Funded Research===<br />
<br />
===National and International Impact===<br />
<br />
<br />
= Timeline (Ross)=<br />
<br />
<The table needs to be updated><br />
<br />
<br />
This section of the report gives the milestones for years 1 through 5 that are associated with the timelines in the original proposal. We have organized the milestones by core. For each milestone we have indicated the proposed year of completion and a very brief description of the current status. In some cases the milestones include ongoing work, and we have try to indicate that in the status. We have also included tables that list any significant changes to the proposed timelines. On the wiki page, we have links to the notes from the various PIs that give more details on their progress and the status of the milestones.<br />
<br />
'''These tables demonstrate that the project is, on the whole, proceeding according to the originally planned schedule.'''<br />
<br />
<br />
<br />
== Core 1: Algorithms ==<br />
<br />
=== Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''MIT'''<br />
| 1<br />
| '''Shape-based segmentation'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 1.1<br />
| Methods to learn shape representations<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 1.2<br />
| Shape in atlas-driven segmentation<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 1.3<br />
| Validate and refine approach<br />
| Year 5<br />
| In Progress<br />
|-<br />
| '''MIT'''<br />
| 2<br />
| '''Shape analysis'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 2.1<br />
| Methods to compute statistics of shapes<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 2.3<br />
| Validation of shape methods on application data<br />
| Year 5<br />
| Completed, refinements ongoing<br />
|-<br />
| '''MIT'''<br />
| 3<br />
| '''Analysis of DTI data'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 3.1<br />
| Fiber geometry<br />
| Year 3<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 3.2<br />
| Fiber statistics<br />
| Year 5<br />
| Completed, new developments ongoing<br />
|-<br />
| '''MIT'''<br />
| 3.3<br />
| Validation on real data<br />
| Year 5<br />
| Completed, refinements ongoing<br />
|-<br />
| '''Utah'''<br />
| 1<br />
| '''Processing of DTI data'''<br />
|<br />
|<br />
|-<br />
| '''Utah'''<br />
| 1.1<br />
| Filtering of DTI<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''Utah'''<br />
| 1.2<br />
| Quantitative analysis of DTI<br />
| Year 3<br />
| Completed, refinements ongoing<br />
|-<br />
| '''Utah'''<br />
| 1.3<br />
| Segmentation of cortex/WM<br />
| Year 3<br />
| Completed partially, modified below<br />
|-<br />
| '''Utah'''<br />
| 1.4<br />
| Segmentation analysis of white matter tracts<br />
| Year 3<br />
| Completed, applications ongoing<br />
|-<br />
| '''Utah'''<br />
| 1.5<br />
| Joint analysis of DTI and functional data<br />
| Year 5<br />
| Initiated<br />
|-<br />
| '''Utah'''<br />
| 2<br />
| Nonparametric Shape Analysis<br />
| Year 5<br />
| Completed<br />
|-<br />
| '''Utah'''<br />
| 2.1<br />
| Framework in place<br />
| Year 3<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.2<br />
| Demonstration on shape of neuranatomy (from Core 3)<br />
| Year 4<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.3<br />
| Development for multiobject complexes<br />
| Year 4<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.4<br />
| Demonstration of NP shape representations on clinical hypotheses from Core 3<br />
| Year 5<br />
| Complete, publications in progress<br />
|-<br />
| '''Utah'''<br />
| 2.6<br />
| Integration into NAMIC-kit<br />
| Year 5<br />
| Incomplete (initiated)<br />
|-<br />
| '''Utah'''<br />
| 2.7<br />
| Shape regression<br />
| Year 5<br />
| Incomplete<br />
|-<br />
<br />
|-<br />
| '''UNC'''<br />
| 1<br />
| '''Statistical shape analysis'''<br />
|<br />
|<br />
|-<br />
| '''UNC'''<br />
| 1.1<br />
| Comparative anal. of shape anal. schemes<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 1.3<br />
| Statistical shape analysis incl. patient variable<br />
| Year 5<br />
| Complete, refinements ongoing<br />
|-<br />
| '''UNC'''<br />
| 2<br />
| '''Structural analysis of DW-MRI'''<br />
|<br />
|<br />
|-<br />
| '''UNC'''<br />
| 2.1<br />
| DTI tractography tools<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 2.2<br />
| Geometric characterization of fiber tracts<br />
| Year 5<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 2.3<br />
| Quant. anal. of diffusion along fiber tracts<br />
| Year 5<br />
| Completed.<br />
|-<br />
| '''GaTech'''<br />
| 1.1<br />
| ITK Implementation of PDEs<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.1<br />
| Applications to Core 3 data<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.2<br />
| New statistic models<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.2<br />
| Shape anaylsis<br />
| Year 4<br />
| Completed, refinements ongoing<br />
|-<br />
| '''GaTech'''<br />
| 2.0<br />
| Integration in to Slicer<br />
| Year 4-5<br />
| Preliminary results and ongoing<br />
|-<br />
| '''MGH'''<br />
| 1<br />
| '''Registration'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 1.1<br />
| Collect DTI/QBALL data<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 1.2<br />
| Develop registration method<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 1.3<br />
| Test/optimize registration method<br />
| Year 3<br />
| In Progress<br />
|-<br />
| '''MGH'''<br />
| 1.4<br />
| Apply registration on core 3 data<br />
| Year 5<br />
| In Queue<br />
|-<br />
| '''MGH'''<br />
| 2<br />
| '''Group DTI Statistics'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 2.1<br />
| Develop group statistic method<br />
| Year 2<br />
| Partially Complete<br />
|-<br />
| '''MGH'''<br />
| 2.2<br />
| Apply on core 3 data<br />
| Year 5<br />
| In Queue<br />
|-<br />
| '''MGH'''<br />
| 3<br />
| '''Diffusion Segmentation '''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 3.1<br />
| Collect DTI/QBALL data<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 3.2<br />
| Develop/optimize segmentation algorithm<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 3.3<br />
| Integrate w/ tractography<br />
| Year 4<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 3.4<br />
| Apply on core 3 data<br />
| Year 5<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4<br />
| '''Group Morphometry Statistics'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 4.1<br />
| Develop/optimize statistics algorithms<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.2<br />
| Develop GUI for Linux<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.3<br />
| Slicer integration<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.4<br />
| Compile application on Windows<br />
| Year 4<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 5<br />
| XNAT Desktop<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 5.1<br />
| Establish requirements for desktop version of XNAT <br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 5.2<br />
| Develop implementation plan for prototype<br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 5.3<br />
| Implement prototype version <br />
| Years 4-5<br />
| Incomplete (in progress)<br />
|-<br />
| '''MGH'''<br />
| 5.4<br />
| Implement alpha version<br />
| Year 5<br />
| Incomplete<br />
|-<br />
| '''MGH'''<br />
| 6<br />
| XNAT Central<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 6.1<br />
| Deploy XNAT Central, a public access XNAT host <br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 6.2<br />
| Coordinate with NAMIC sites to upload project data<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 6.3<br />
| Continue developing XNAT Central based on feedback from NAMIC sites<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 7<br />
| NAMIC Kit integration<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 7.1<br />
| Implement web services to exchange data with Slicer, Batchmake, and other client applications<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 7.2<br />
| Add XNAT Desktop to standard NAMIC kit distribution<br />
| Year 5<br />
| Incomplete<br />
|-<br />
|}<br />
<br />
=== Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| '''MIT'''<br />
| 2.2<br />
| Methods to compare shape statistics<br />
| Removed, the effort refocused on registration necessary for population studies <br />
|-<br />
| '''MIT'''<br />
| 2.4<br />
| Software infrastructure to integrate shape analysis tools into the pipeline for population studies.<br />
| New, morphed into collaboration with XNAT to provide more general population analysis tools. Partially completed.<br />
|-<br />
| '''MIT'''<br />
| 4<br />
| fMRI analysis including local and atlas-based priors for quantifying activation.<br />
| New, partially completed. Refinements in progress. Clinical study with Core 1 is in progress.<br />
|-<br />
| '''Utah'''<br />
| 2.2 (removed)<br />
| Feature-based brain image registration.<br />
| Shift emphasis to shape-based analysis/registration<br />
|-<br />
| '''Utah'''<br />
| 2.1 (removed)<br />
| Cortical filtering and feature detection<br />
| Effort is subsumed by other Core 1 partners (e.g. see MGH/Freesurfer)<br />
|-<br />
| '''Utah'''<br />
| 1.3 (removed)<br />
| Segmentation of cortex/WM<br />
| Effort is subsumed by other Core 1-2 partners (e.g. see EM-Segmenter)<br />
|-<br />
| '''Utah'''<br />
| 3.0 (removed)<br />
| Fast implmentations of PDEs<br />
| Real-time filtering is demphasized in favor of shape/DTI analysis<br />
|-<br />
| '''Utah'''<br />
| 1.5 (added)<br />
| Joint analysis of DTI and functional data<br />
| Opportunities/needs within various collaborations<br />
|-<br />
| '''Utah'''<br />
| 2.1-2.3 (added, in place of cortical analysis)<br />
| Shape analysis<br />
| Nonparametric shape analysis added to address needs of core 3.<br />
|-<br />
| '''Utah'''<br />
| 2.7<br />
| Shape regression<br />
| Extension/completion of framework. Opportunities/needs within various collaborations.<br />
|-<br />
| '''UNC'''<br />
| 1.2<br />
| Develop medially-based shape representation<br />
| Remove<br />
|-<br />
| '''UNC'''<br />
| 1.4<br />
| Develop generic cortical correspondence framework (Years 3-5)<br />
| New<br />
|-<br />
| '''UNC'''<br />
| 2.4<br />
| DTI Atlas Building (Years 2--4)<br />
| New<br />
|-<br />
| '''GaTech'''<br />
| 2.1<br />
| FA analysis<br />
| New<br />
|-<br />
| '''MGH'''<br />
| 4.1 - 4.4 <br />
| Group Morphometry Statistics<br />
| Added and then removed, based on personnel changes<br />
|-<br />
| '''MGH'''<br />
| 5-7 <br />
| XNAT<br />
| Added to support remote image database capabilities<br />
|}<br />
<br />
=== [[Core_1_Timeline_Notes|Core 1 Timeline Notes ]] ===<br />
<br />
== Core 2: Engineering ==<br />
<br />
=== Core 2 Timelines and Milestones ===<br />
<br />
<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''GE'''<br />
| 1<br />
| '''Define software architecture'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Object design<br />
| Yr 1<br />
| Completed<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Identify patterns<br />
| Yr 3<br />
| Patterns for processing scalar and vector images, models, fiducials complete. Patterns for diffusion weighted completed, fMRI ongoing.<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Create frameworks<br />
| Yr 3<br />
| Frameworks for processing scalar and vector images, models, fiducials complete. Frameworks for diffusion weighted completed, fMRI ongoing.<br />
|-<br />
| '''GE'''<br />
| 2<br />
| '''Software engineering process'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Extreme programming<br />
| Yr 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Process automatiion<br />
| Yr 3<br />
| On schedule, ongoing<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Refactoring<br />
| Yr 3<br />
| Complete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| '''Automated quality system'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 3<br />
| DART deployment<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| Persistent testing system<br />
| Yr 5<br />
| Incomplete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| Automatic defect detection<br />
| Yr 5<br />
| Incomplete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| '''Cross-platform development'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Deploy environment (CMake, CTest)<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| DART Integration and testing<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Documentation tools<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| '''Integration tools'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| File Formats/IO facilities<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| CableSWIG deployment<br />
| Yr 3<br />
| Complete (integration ongoing)<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Establish XML schema<br />
| Yr 4<br />
| Complete, refinements ongoing<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| '''Technology delivery'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Deploy applications<br />
| Yr 1<br />
| Complete (ongoing)<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Establish plug-in repository<br />
| Yr 2<br />
| Incomplete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Cpack<br />
| Yr 4-5<br />
| Incomplete<br />
|-<br />
| '''Isomics'''<br />
| 1<br />
| NAMIC builds of slicer<br />
| Years 2--5<br />
| Complete<br />
|-<br />
| '''Isomics'''<br />
| 1<br />
| Schizophrenia and DBP intefaces<br />
| Year 3---5<br />
| Completed (refinements ongoing)<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| ITK Integration tools<br />
| Year 1---3<br />
| Completed<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| Experiment Control Interfaces<br />
| Year 2---5<br />
| Migration from LONI to BatchMake Underway<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| fMRI/DTI algorithm support<br />
| Year 2---5<br />
| Completed DTI, fMRI Ongoing<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| New DBP algorithm support<br />
| Year 2---5<br />
| Ongoing<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Compatible build process<br />
| Year 1---3<br />
| Completed<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Dart Integration<br />
| Year 1---2<br />
| Completed (upgrades ongoing)<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Test scripts for new code<br />
| Year 2---5<br />
| Ongoing<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Grid computing---base<br />
| Year 1<br />
| Completed<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Grid enabled algorithms<br />
| Year 3<br />
| First version (GWiz alpha) available - initial integration with Slicer3 and execution model.<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Testing infrastructure<br />
| Year 4<br />
| Initiated<br />
|-<br />
| '''UCSD'''<br />
| 2<br />
| Data grid --- compatibility<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''UCSD'''<br />
| 2<br />
| Data grid --- slicer access<br />
| Year 2<br />
| Completed for version 2.6. In progress for Slicer3<br />
|-<br />
| '''UCSD'''<br />
| 3<br />
| Data mediation --- deploy<br />
| Year 1<br />
| Incomplete (modfication below)<br />
|-<br />
| '''UCLA'''<br />
| 1<br />
| Debabeler functionality<br />
| Year 1<br />
| Continued Progress<br />
|-<br />
| '''UCLA'''<br />
| 2<br />
| SLIPIE Interpretation (Layer 1)<br />
| Year 1--Year2<br />
| In Progress<br />
|-<br />
| '''UCLA'''<br />
| 3<br />
| SLIPIE Interpretation (Layer 2)<br />
| Year 1--Year2<br />
| On Schedule<br />
|-<br />
| '''UCLA'''<br />
| 3<br />
| Developing ITK Modules<br />
| Year2<br />
| In Progress<br />
|-<br />
| '''UCLA'''<br />
| 4<br />
| Integrating SRB (GSI-enabled)<br />
| Year2<br />
| Completed<br />
|-<br />
| '''UCLA'''<br />
| 5<br />
| Integrating IDA<br />
| Year2<br />
| Completed<br />
|-<br />
| '''UCLA'''<br />
| 5<br />
| Integrating External Visualization Applications<br />
| Year2<br />
| Completed<br />
|}<br />
<br />
=== Core 2 Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Data mediation<br />
| Delayed pending integration of databases into NAMIC infractructure<br />
|}<br />
<br />
=== [[Core_2_Timeline_Notes|Core 2 Timeline Notes ]] ===<br />
<br />
== Core 3: Driving Biological Problems ==<br />
<br />
The Core 3 projects submitted R01 style proposals, as specified in the RFA, and did not submit timelines.<br />
<br />
== Core 4: Service ==<br />
<br />
=== Core 4 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| '''Implement Development Farms'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Deploy platforms<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Communications<br />
| Yrs 1<br />
| Complete, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| '''Establish software process'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Secure developer database<br />
| Yr 1<br />
| Complete, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Collect guidelines<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Manage software submission process<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Configure process tools<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Survey community<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| '''Deploy NAMIC Tools'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Toolkits<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Integration tools<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Applications<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Integrate new computing resources<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| '''Provide support'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| Esablish support infrastructure<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| NAMIC support<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 5<br />
| Manage NAMIC Software Releases<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|}<br />
<br />
=== Core 4 Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| Kitware<br />
| 2-5<br />
| Various<br />
| Refined/modified the sub aims<br />
|}<br />
<br />
=== [[Core_4_Timeline_Notes|Core 4 Timeline Notes ]] ===<br />
<br />
== Core 5: Training ==<br />
<br />
=== Core 5 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| '''Formal Training Guidllines'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| Functional neuroanatomy<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| Clinical correlations<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| '''Mentoring'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| Programming workshops<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| One-on-one mentoring, Cores 1, 2, 3<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| '''Collaborative work environment'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Wiki<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Mailing lists<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Regular telephone conferences<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 4<br />
| '''Educational component for tools'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 4<br />
| Slicer training modules<br />
| Yr 2-5<br />
| Slicer 2.x tutorials complete, Two Slicer 3 tutorials complete, translation of 2.x tutorials to 3 is ongoing and on schedule<br />
|-<br />
| '''Harvard'''<br />
| 5<br />
| '''Demonstrations and hands-on training'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 5<br />
| Various workshops and conferences<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|}<br />
<br />
=== Core 5 Timeline Modifications ===<br />
<br />
None.<br />
<br />
=== [[Core_5_Timeline_Notes|Core 5 Timeline Notes ]] ===<br />
<br />
== Core 6: Dissemination ==<br />
<br />
=== Core 6 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| Isomics<br />
| 1<br />
| Create a collaboration metholdology for NA-MIC<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 1.1<br />
| develop a selection process<br />
| Yr 1<br />
| Complete<br />
|-<br />
| Isomics<br />
| 1.2<br />
| guidelines to govern the collaborations<br />
| Yr 1-2<br />
| Complete<br />
|-<br />
| Isomics<br />
| 1.3<br />
| Provide on-site training<br />
| Yr 1-5<br />
| Complete for current tools (ongoing for tool refinement)<br />
|-<br />
| Isomics<br />
| 1.4<br />
| develop a web site infrastructure<br />
| Yr 1<br />
| Complete<br />
|-<br />
| Isomics<br />
| 2<br />
| Facilitate communication between NA-MIC developers and wider research community<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 2.1<br />
| develop materials describing NAMIC technology<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.2<br />
| participate in scientific meetings<br />
| Yr 2-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.3<br />
| Document interactions with external researchers<br />
| Yr 2-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.4<br />
| Coordinate publication strategies<br />
| Yr 3-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3<br />
| Develop a publicly accessible internet resource of data, software, documentation, and publication of new discoveries<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 3.1<br />
| On-line repository of NAMIC related publications and presentations<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3.2<br />
| On-line repository of NAMIC tutorial and training material<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3.3<br />
| Index and a searchable database<br />
| Yr 1-2<br />
| Done<br />
|-<br />
| Isomics<br />
| 3.4<br />
| Automated feedback systems that track software downloads<br />
| Yr 3<br />
| Done<br />
|}<br />
<br />
=== Core 6 Timeline Modifications ===<br />
<br />
None.<br />
<br />
=== [[Core_6_Timeline_Notes|Core 6 Timeline Notes ]] ===<br />
<br />
=Appendix A Publications (Mastrogiacomo)=<br />
A list should be mined from the publications database and attached here in MS word format.<br />
<br />
=Appendix B EAB Report and Response (Kapur)=<br />
===EAB Report===<br />
<br />
===Response to EAB Report===</div>Kubickihttps://www.na-mic.org/w/index.php?title=2009_Annual_Scientific_Report&diff=362922009 Annual Scientific Report2009-04-15T17:55:14Z<p>Kubicki: /* Clinical Component (Kubicki) */</p>
<hr />
<div>Back to [[2009_Progress_Report]]<br />
<br />
<br />
<br />
<br />
<br />
=Guidelines for preparation=<br />
<br />
*[[2009_Progress_Report#Scientific Report Timeline]] - Main point is that May 15 is the date by which all sections below need to be completed. No extensions are possible.<br />
*DBPs - If there is work outside of the roadmap projects that you would like to report, you are welcome to create a separate section for it under "Other". <br />
*The outline for this report is similar to the 2008 and 2007 reports, which are provided here for reference: [[2008_Annual_Scientific_Report]], [[2007_Annual_Scientific_Report]].<br />
*In preparing summaries for each of the 8 topics in this report, please leverage the detailed pages for projects provided here: [[NA-MIC_Internal_Collaborations]].<br />
*Publications will be mined from the SPL publications database. All core PIs need to ensure that all NA-MIC publications are in the publications database by May 15.<br />
<br />
=Introduction (Tannenbaum)=<br />
<br />
The National Alliance for Medical Imaging Computing (NA-MIC) is now in its fifth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. This was our second year with our current DBPS of which three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. The and fourth is a very new direction, the prostate: brachytherapy needle positioning robot integration.<br />
<br />
We briefly summarize the work of NAMIC during the five years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main themes: Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. The third year of center activity was devoted to the continuation of the collaborative efforts in order to give solutions to the various brain-oriented DBPs. The fourth year was focused on translating our work to the new DBPs. In the fifth year, a number of projects reached the point where modules were introduced<br />
into Slicer making the Core 1 algorithms available to the general medical imaging community. A number of the algorithms are quite general and can be used for purposes much broader than the original DBPs. For example, a new point cloud registration algorithm was developed for the prostate brachytherapy needle positioning project can be used also for DWI registration.<br />
Work on DTI/DWI tractography has impacted the segmentation of blood vessels and soft plaque detection in the coronaries.<br />
<br />
Year five has seen progress with the work of our current DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.<br />
<br />
In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent work on the four infrastructure topics. These include: Diffusion Image analysis (Section 4.1), Structural analysis (Section 4.2), Functional MRI analysis (Section 4.3), and the NA-MIC Toolkit (Section 4.4). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final sections of this report, Sections 5-11, provide updated timelines on the status of the various projects of the different cores of NAMIC.<br />
<br />
=Clinical Roadmap Projects=<br />
==Roadmap Project: Stochastic Tractography for VCFS (Kubicki)==<br />
===Overview (Kubicki)===<br />
The goal of this project is to create an end-to-end application that would be useful in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-facial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.<br />
<br />
===Algorithm Component (Golland)===<br />
At the core of this project is the stochastic tractography algorithm<br />
developed and implemented in collaboration between MIT and<br />
BWH. Stochastic Tractography is a Bayesian approach to estimating<br />
nerve fiber tracts from DTI images.<br />
<br />
We first use the diffusion tensor at each voxel in the volume to<br />
construct a local probability distribution for the fiber direction<br />
around the principal direction of diffusion. We then sample the tracts<br />
between two user-selected ROIs, by simulating a random walk between<br />
the regions, based the local transition probabilities inferred from<br />
the DTI image.<br />
<br />
The resulting collection of fibers and the associated FA values<br />
provide useful statistics on the properties of connections between the<br />
two regions. To constrain the sampling process to the relevant white<br />
matter region, we use atlas-based segmentation to label ventricles and<br />
gray matter and to exclude them from the search space. As such, this<br />
step relies heavily on the registration and segmentation functionality<br />
in Slicer.<br />
<br />
Over the last year, we have been working on applying several pre- and postprocessing steps to the algorithm pipeline. These steps include eddy current and geometric distortion correction that have been made available to us by Utah group, as well as DTI filtering (BWH). White matter masks can also now be created based on T2 thresholding within the slicer stochastic tractography module, which makes them more precise, since they do not rely on MRI to DTI co-registration. <br />
<br />
At the same time we are working on the datasets where fMRI activations as well as gray matter segmentations need to be registered to DTI data, in order to seed within the predefined gray matter regions. We have made a significant progress in between modality registration, additional improvement is expected when distortion correction become part of the analysis pipeline. <br />
<br />
We are also working on improved ways to visualize and quantify stochastic tractography output, not only by parametrizing fiber tracts, but also by creating connection probability distribution maps.<br />
<br />
===Engineering Component (Davis)===<br />
Stochastic Tractography slicer module has been rewritten in python now, and new module released in December 2008, and presented at the AHM in SLC. Its now part of the slicer3. Module documentation have been also created. Current engineering efforts are concentrated on maintaining the module, optimizing it for working with other data formats, and adding new functionality, such as better registration, distortion correction and ways of extracting and measuring FA along the tracts. <br />
<br />
Also, because of the fact that the new data is much more computationally demanding (higher spatial resolution, more diffusion directions), and cortical ROIs usually much larger than the previously used WM ROIs, there is general need for performance improvement. This issue is highlighted especially by our stochastic way of tracking connections, where hundreds, instead of just one, (as in deterministic tractography) tracts are being generated from one seed. Thus some of our efforts go towards multithreading, and utilizing parallel processing. Version of our algorithm that uses large computer clusters have been developed and can be downloaded and installed by individual users with minimal knowledge of parallel computing now.<br />
<br />
===Clinical Component (Kubicki)===<br />
Over the last year, we tested the algorithm on newly released 3T NAMIC data, which contains high resolution DTI as well as structural RM data, plus automatic anatomical segmentations. Data is already co-registered, so cortical ROIs can be used as seeding points for stochastic tractography. <br />
<br />
Using this dataset, we have completed a clinical study, where we looked at the connections between inferior frontal and superior temporal lobes, sites of the language network. Connections of these two regions, obtained with stochastic tractography, have been measured, and compared between group of 20 chronic schizophrenia patients and 20 controls. We have also looked at gray matter volumes of destination regions, trying to estimate relationship between gray and white matter abnormalities in schizophrenia. Results of this study have been presented at World Psychiatry Congress in Florence, Italy in April of 2009, as well as at Harvard Psychiatry MYSELL conference also in April 2009. <br />
<br />
Another clinical study that is under way, is the application of stochastic tractography to define connections involved in emotional processing. For this purpose, we use cortical segmentations of anterior cingulated gyrus, orbital-frontal gyrus and amygdala, and trace as well as quantify connections between there regions in healthy controls as well in schizophrenia patients. Results of this preliminary study have been presented at MYSELL in April 2009, and will be presented at Biological Psychiatry conference later this year. <br />
<br />
We are also involved in two collaborative studies. In one, use DTI data acquired in at UCI, and apply stochastic method to segment and measure arcuate fasciculus in subjects with schizophrenia and language impairment, as evinced in ERP data. In another collaboration, we combine resting state fMRI data with DTI in order to measure connectivity between regions forming functional network. Both these projects are under way. <br />
<br />
Finally, stochastic tractography have been used qualitatively in one publication that is in press in Human Brain Mapping. Here, we combined fMRI with DTI whole brain data analysis, and found regions that were expressing abnormal functional connectivity in schizophrenia. These regions were then assigned to certain anatomical structures (white mater tracts), based on their location, and relationship to stochastic tractography output.<br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard:Brain_Segmentation_Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==<br />
===Overview (Fichtinger)===<br />
Numerous studies have demonstrated the efficacy of image-guided<br />
needle-based therapy and biopsy in the management of prostate<br />
cancer. The accuracy of traditional prostate interventions performed using<br />
transrectal ultrasound (TRUS) is limited by image fidelity, needle<br />
template guides, needle deflection and tissue deformation. Magnetic Resonance<br />
Imaging (MRI) is an ideal modality for guiding and monitoring<br />
such interventions due to its excellent visualization of the prostate, its<br />
sub-structure and surrounding tissues. <br />
<br />
We have designed a comprehensive robotic assistant system that allows prostate biopsy and brachytherapy procedures to be performed entirely inside a 3T closed MRI scanner. The current system applies transrectal approach to the prostate: an endorectal coil and steerable needle guide, both tuned to 3T magnets and invariable to any particular scanner, are integrated into the MRI compatible manipulator.<br />
<br />
Under the NAMIC initiative, the image computing, visualization, intervention planning, and kinematic planning interface is being accomplished with open source system built on the NAMIC toolkit and its components, such as Slicer3 and ITK. These are complemented by a collection of unsupervised prostate segmentation and registration methods that are of great importance to the clinical performance of the interventional system as a whole.<br />
<br />
===Algorithm Component (Tannenbaum)===<br />
<br />
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps below.<br />
<br />
'''Prostate Segmentation'''<br />
<br />
We must first extract the prostate. We provided two methods: a shape based method and a semi-automatic method. More details are given below and images and further details may be found [http://www.na-mic.org/Wiki/index.php/Projects:ProstateSegmentation here]<br />
<br />
# ''A shape based algorithm''. This begins with learning a group of shapes, obtained from manually segmenting a set of prostate 3D images. With the shapes represented as the hyperbolic tangent of the signed distance functions, principle component analysis is employed to learn the shapes. Further, given a new prostate image, we search the learned shape space in order to find one shape best segment the given image. The fitness of one shape to segment the image is evaluated by an energy functional measuring the discrepancy of the statistical characteristics inside and outside the current segmentation boundary. Such method is robust to the noise in the images. Moreover, the whole algorithm pipeline has been integrated into the Slicer3 through the command line module.<br />
# ''Semi-automatic method''. This method is based on a random walk segmentation algorithm. With user provided initial seed regions inside and out side the object (prostate), the algorithm computes a probability distribution over the image domain by solving a boundary value partial differential equation where the value at seed regions are fixed at 1.0 or 0.0, depending or whether they are object or background seeds. The resulting distribution indicates the probability of each voxel belonging to the object. Simply threshold by 0.5 gives the segmentation of the object. Moreover, if the result is not suitable, the user can edit the seed regions, and the new result is computed based on this previous result. This algorithm has been integrated into the transrectal prostate MRI module of Slier3.<br />
<br />
'''Prostate Registration'''<br />
<br />
We developed a nonlinear (affine) prostate registration method by treating prostate images as point sets. Then the iterative closest point algorithm is improved to register the point sets generated by the two images to be registered. The proposed method shows robustness to long distance transition and partial image structure. Moreover, such representation is much sparser than sampling image on the uniform grid thus the registration is very fast comparing two 3D volumetric<br />
image registration.<br />
<br />
Furthermore, the registration is viewed as a posterior estimation problem, in which the distributions of the affine and translation parameters are to be estimated. This can naturally be estimated using a particle filter framework. Through this, the method can handle the otherwise difficult cases where the two prostates are one supine and<br />
one prone.<br />
<br />
More details are given [[Projects:pfPtSetImgReg|here...]]<br />
<br />
===Engineering Component (Hayes)===<br />
<Note Progress in the last year><br />
<br />
<br />
===Clinical Component (Fichtinger)===<br />
<Note Progress in the last year><br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:JHU:Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus (Bockholt)==<br />
===Overview (Bockholt)===<br />
The primary goal of the MIND DPB is to examine changes in white matter lesions in adults with Neuropsychiatric Systemic Lupus Erythematosus (SLE). We want to be able to characterize lesion location, size, and intensity, and would also like to examine longitudinal changes of lesions in an SLE cohort. To accomplish this goal, we will create an end-to-end application entirely within NA-MIC Kit allowing individual analysis of white matter lesions. Such a workflow will then be applied to a clinical sample in the process of being collected.<br />
<br />
===Algorithm Component (Whitaker)===<br />
The basic steps necessary for the white matter lesion analysis application entail first registration of T1, T2, and FLAIR images, second tissue classification into gray, white, csf, or lesion, thirdly clustering lesion for anatomical localization, and finally a summarization of lesion size and image intensity parameters within each unique lesion. <br />
<br />
<Note Progress in the last year><br />
<br />
===Engineering Component (Pieper)===<br />
<Note Progress in the last year><br />
<br />
===Clinical Component (Bockholt)===<br />
<Note Progress in the last year><br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND:Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Cortical Thickness for Autism(Hazlett)== <br />
===Overview (Hazlett)===<br />
<br />
A primary goal of the UNC DPB is to examine changes in cortical thicknes in children with autism compared to typical controls. We want to examine group differences in both local and regional cortical thickness, and would also like to examine longitudinal changes in the cortex from ages 2-4 years. To accomplish this goal, this project will create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness. Such a workflow will then be applied to our study data (already collected).<br />
<br />
===Algorithm Component (Styner)===<br />
<br />
The basic steps necessary for the cortical thickness application entail first tissue segmentation in order to separate white and gray matter regions, second cortical thickness measurement, thirdly cortical correspondence to compare measurements across subjects and finally a statistical analysis to locally compute group differences.<br />
<br />
<Note Progress in the last year><br />
<br />
===Engineering Component (Miller, Vachet)===<br />
<br />
<Note Progress in the last year><br />
<br />
===Clinical Component (Hazlett)===<br />
<Note Progress in the last year><br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNC:Cortical_Thickness_Roadmap here on the NA-MIC wiki].<br />
<br />
=Four Infrastructure Topics=<br />
==Diffusion Image Analysis (Gerig)==<br />
<Note Progress in the last year><br />
===Key Investigators===<br />
<br />
<Need to update the list below><br />
<br />
* BWH: Marek Kubicki, Martha Shenton, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Carl-Fredrik Westin, Gordon Kindlmann<br />
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo<br />
* UCI: James Fallon<br />
* Utah I: Tom Fletcher, Ross Whitaker, Ran Tao, Yongsheng Pan<br />
* Utah II: Casey Goodlett, Sylvain Gouttard, Guido Gerig<br />
* GA Tech: John Melonakos, Vandana Mohan, Shawn Lankton, Allen Tannenbaum<br />
* GE: Xiaodong Tao, Jim Miller<br />
* Isomics: Steve Pieper<br />
* Kitware: Luis Ibanez<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:DiffusionImageAnalysis here on the NA-MIC wiki].<br />
<br />
==Structural Analysis(Tannenbaum)==<br />
===Progress===<br />
Under Structural Analysis, the main topics of research for NAMIC are structural segmentation, registration techniques and shape analysis. These topics are correlated and hence research in one often finds application in another. For example, shape analysis can yield useful priors for segmentation, or segmentation and registration can provide structural correspondences for use in shape analysis and so on. <br />
<br />
An overview of selected progress highlights under these broad topics follows:<br />
<br />
Segmentation<br />
<br />
* Geodesic Tractography Segmentation: We proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. This has been applied successfully to the segmentation of neural fiber bundles such as the Cingulum Bundle. This framework has now been integrated into Slicer and is being tested on a population of brain data sets.<br />
<br />
* Tubular Surface Segmentation: We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels. <br />
<br />
* Local-global Segmentation: We have proposed a novel segmentation approach that combines the advantages of local and global approaches to segmentation, by using statistics over regions that are local to each point on the evolving countour. This makes it well suited to applications with contrast differences within the structure of interest such as in blood vessel segmentation, as well as applications like the neural fiber bundles where the diffusion profiles of voxels within the structure are locally similar but vary along the length of the fiber bundle itself.<br />
<br />
* Shape-based segmentation: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have presented an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. We have applied the algorithm successfully to real MRI images, and we have also implemented it into 3D Slicer.<br />
<br />
* Re-Orientation Approach for Segmentation of DW-MRI: This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation which allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares very favorably with segmentation by full-brain streamline tractography. <br />
<br />
<br />
Registration<br />
<br />
* Optimal Mass Transport based Registration: We have provided a computationaly efficient non-rigid/elastic image registration algorithm based on the Optimal Mass Transport theory. We use the Monge-Kantorovich formulation of the Optimal Mass Transport problem and implement the solution proposed by Haker et al. using multi-resolution and multigrid techniques to speed up the convergence. We also leverage the computation power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We extend the work by Haker et al. who compute the optimal warp from a first order partial differential equation, an improvement over earlier proposed higher order methods and those based on linear programming, and further implement the algorithm using a coarse-to-fine strategy resulting in phenomenol improvement in convergence. We have applied it successfully to the registration of 3D brain MRI datasets (preoperative and intra-operative), and are currently extending it to the non-rigid registration of baseline DWI to brain MRI data.<br />
<br />
* Atlas Regularization for Image Segmentation: Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. <br />
<br />
* Point-set Rigid Registration: We have proposed a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation. Moreover, we incorporate stochastic dynamics to model the uncertainity of the registration process. We treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temoral coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets.We applied the algorithm to different alignments of point clouds and it successfully found the correct optimal transformation that aligns two given point clouds despite the differing geometry around the local neighborhood of a point within their respective sets. <br />
<br />
* Regularization for Optimal Mass Transport: To extend the flexibility of the existing OMT algorithm, we added a regularization term to the functional being minimized. This term controls the tradeoff between how well two images match after registration versus how warped the transformation map can become. A weighted sum of squared differences is used to penalize having to move mass over long distances; this addition also helps to keep the transformation physically accurate by reducing the likelihood that the transformation grid will fold over itself and keeping the grid smooth.<br />
<br />
* Registration of DW-MRI to structural MRI: Optimal Mass Transport was applied to the problem of correcting EPI distortion in DW-MRI. A mask for white matter in DW-MRI was registered to the white matter mask extracted from the structural MRI for the same patient. Prior to registration, it is important to normalize intensities in the two masks; this was done by dividing the images into regions and uniformly normalizing over each region to assure the sum of the intensities is equal. Then, once a transformation between the white matter masks was calculated, this transformation was applied to the original DW-MRI image. <br />
<br />
Shape Analysis<br />
<br />
* Shape Analysis Framework using SPHARM-PDM: We have provided an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are first processed to fill any interior holes. The processed binary segmentations are converted to surface meshes, and a spherical parametrization is computed for the surface meshes using a area-preserving, distortion minimizing spherical mapping. The SPHARM description is computed from the mesh and its spherical parametrization. Using the first order ellipsoid from the spherical harmonic coefficients, the spherical parametrizations are aligned to establish correspondence across all surfaces. The SPHARM description is then sampled into a triangulated surfaces (SPHARM-PDM) via icosahedron subdivision of the spherical parametrization. These SPHARM-PDM surfaces are all spatially aligned using rigid Procrustes alignment. Group differences between groups of surfaces are computed using the standard robust Hotelling T 2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. We provide additional visualization of the group tests via mean difference magnitude and vector maps, as well as maps of the group covariance information. We have a stable implementation, and current development focuses on integrating the current command line tools into Slicer (v3) via the Slicer execution model. <br />
<br />
* Population studies using Tubular Surface Model: We have proposed a tubular shape model for the Cingulum Bundle which models a tubular surface as a center-line coupled with a radius function at every point along the center-line. This model shows potential for population studies on the Cingulum Bundle which is believed to be involved in Schizophrenia, since it provides a natural way of sampling the structure to build a feature representation of it. We are currently segmenting the Cingulum Bundle from a population of brain data sets, towards performing this population analysis using the Pott's Model.<br />
<br />
* Automatic Outlining of Sulci on a Brain Surface: We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain, where the data is taken to be a 3D triangulated mesh formed from the segmentation of MR image slices. The problem is posed as energy minimization using penalizing the arc-length of segmenting curve using conformal factor involving the mean curvature of the underlying surface. The computation is made practical for dense meshes via the use of a sparse-field method to track the level set interfaces and regularized least-squares estimation of geometric quantities.<br />
<br />
===Key Investigators===<br />
<br />
Needs to be updated:<br />
<br />
* MIT: Polina Golland, Kilian Pohl, Sandy Wells, Eric Grimson, Mert R. Sabuncu<br />
* UNC: Martin Styner, Ipek Oguz, Xavier Barbero <br />
* Utah: Ross Whitaker, Guido Gerig, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer <br />
* GaTech: Allen Tannenbaum, John Melonakos, Vandana Mohan, Tauseef ur Rehman, Shawn Lankton, Samuel Dambreville, Yi Gao, Romeil Sandhu, Xavier Le Faucheur, James Malcolm, Ivan Kolosev<br />
* Isomics: Steve Pieper <br />
* GE: Bill Lorensen, Jim Miller <br />
* Kitware: Luis Ibanez, Karthik Krishnan<br />
* UCLA: Arthur Toga, Michael J. Pan, Jagadeeswaran Rajendiran <br />
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt, Yogesh Rathi, Marek Kubicki, Steven Haker<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:StructuralImageAnalysis here on the NA-MIC wiki].<br />
==fMRI Analysis (Golland)==<br />
===Progress===<br />
One of the major goals in analysis of fMRI data is the detection of<br />
functionally homogeneous networks in the brain. <br />
<br />
<note progress here><br />
<br />
===Key Investigators===<br />
<br />
Need to update this list:<br />
<br />
# MIT: Polina Golland, Danial Lashkari, Bryce Kim <br />
# Harvard/BWH: Sylvain Bouix, Martha Shenton, Marek Kubicki<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:fMRIAnalysis here on the NA-MIC wiki].<br />
==NA-MIC Kit Theme (Schroeder)==<br />
===Progress===<br />
The NAMIC-Kit consists of a framework of advanced computational components, as well as the support infrastructure for testing, documenting, and deploying leading edge medical imaging algorithms and software tools. The framework has been carefully constructed to provide low-level access to libraries and modules for advanced users, plus high-level application access that non-computer professionals can use to address a variety of problems in biomedical computing. In this fifth year of the NA-MIC projects <summary of progress><br />
<br />
===Software Releases===<br />
The NAMIC-Kit can be represented as a pyramid of capabilities, with the base consisting of toolkits and libraries, and the apex standing in for the Slicer3 user application. In between, Slicer modules are stand-alone executables that can be integrated directly into the Slicer3 application, including GUI integration, while work-flows are groups of modules that are integrated together to manifest sophisticated segmentation, registration and biomedical computing algorithms. In a coordinated NAMIC effort, major releases of these many components were realized over the past year. This includes, but is not limited to:<br />
*<br />
*<br />
<br />
===Slicer3 and the Software Framework===<br />
One of the major achievements of the past year has been...<br />
<br />
===Software Process===<br />
One of the challenges facing developers has been the requirement to implement, test and deploy software systems across multiple computing platforms. NAMIC continues to push the state of the art with further development of the CMake, CTest, and CPack tools for cross-platform development, testing, and packaging, respectively...<br />
<br />
===Key Investigators===<br />
THis list needs to be updated:<br />
<br />
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman<br />
* GE - Jim Miller, Xiaodong Tao<br />
* Isomics - Steve Pieper<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC-Kit here on the NA-MIC wiki].<br />
<br />
<br />
<br />
=Highlights(Schroeder)=<br />
===Advanced Algorithms===<br />
<br />
===NAMIC-Kit===<br />
<br />
===Outreach and Technology Transfer===<br />
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.<br />
* The Slicer community held several workshops and tutorials. In xxx a satellite event was held for the international Organization for Human Brain Mapping at the annual meeting in xxx. The xx workshop on xx hosted xx participants representing xx countries from around the world, xx states within the US and xxdifferent laboratories including xx NIH institutes. In addition, <note how many slicer tutorials were held and where etc><br />
* Project Week continues to be a successful NAMIC venue. These semi-annual events are held in Boston in June, and January in Salt Lake City. These events are well attended with approximately 100 participants, of which about a third are outside collaborators. At the last Project Week in Salt Lake City, approximately xx projects were realized.<br />
* NAMIC continues to participate in conferences and other technical venues. For example, NAMIC hosted xxx<br />
<br />
<br />
=Impact and Value to Biocomputing (Miller)=<br />
NA-MIC impacts Biocomputing through a variety of mechanisms. First,<br />
NA-MIC produces scientific results, methodologies, workflows,<br />
algorithms, imaging platforms, and software engineering tools and<br />
paradigms in an open enviroment that contributes directly to the body of<br />
knowledge available to the field. Second, NA-MIC science and<br />
technology enables the entire medical imaging community to build on<br />
NA-MIC results, methods, and techniques, to concentrate on the new<br />
science instead of developing supporting infrastructure, to leverage<br />
NA-MIC scientists and engineers to adapt NA-MIC technology to new<br />
problem domains, and to leverage NA-MIC infrastructure to distribute<br />
their own technology to a larger community.<br />
<br />
===Impact within the Center===<br />
<br />
===Impact within NIH Funded Research===<br />
<br />
===National and International Impact===<br />
<br />
<br />
= Timeline (Ross)=<br />
<br />
<The table needs to be updated><br />
<br />
<br />
This section of the report gives the milestones for years 1 through 5 that are associated with the timelines in the original proposal. We have organized the milestones by core. For each milestone we have indicated the proposed year of completion and a very brief description of the current status. In some cases the milestones include ongoing work, and we have try to indicate that in the status. We have also included tables that list any significant changes to the proposed timelines. On the wiki page, we have links to the notes from the various PIs that give more details on their progress and the status of the milestones.<br />
<br />
'''These tables demonstrate that the project is, on the whole, proceeding according to the originally planned schedule.'''<br />
<br />
<br />
<br />
== Core 1: Algorithms ==<br />
<br />
=== Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''MIT'''<br />
| 1<br />
| '''Shape-based segmentation'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 1.1<br />
| Methods to learn shape representations<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 1.2<br />
| Shape in atlas-driven segmentation<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 1.3<br />
| Validate and refine approach<br />
| Year 5<br />
| In Progress<br />
|-<br />
| '''MIT'''<br />
| 2<br />
| '''Shape analysis'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 2.1<br />
| Methods to compute statistics of shapes<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 2.3<br />
| Validation of shape methods on application data<br />
| Year 5<br />
| Completed, refinements ongoing<br />
|-<br />
| '''MIT'''<br />
| 3<br />
| '''Analysis of DTI data'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 3.1<br />
| Fiber geometry<br />
| Year 3<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 3.2<br />
| Fiber statistics<br />
| Year 5<br />
| Completed, new developments ongoing<br />
|-<br />
| '''MIT'''<br />
| 3.3<br />
| Validation on real data<br />
| Year 5<br />
| Completed, refinements ongoing<br />
|-<br />
| '''Utah'''<br />
| 1<br />
| '''Processing of DTI data'''<br />
|<br />
|<br />
|-<br />
| '''Utah'''<br />
| 1.1<br />
| Filtering of DTI<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''Utah'''<br />
| 1.2<br />
| Quantitative analysis of DTI<br />
| Year 3<br />
| Completed, refinements ongoing<br />
|-<br />
| '''Utah'''<br />
| 1.3<br />
| Segmentation of cortex/WM<br />
| Year 3<br />
| Completed partially, modified below<br />
|-<br />
| '''Utah'''<br />
| 1.4<br />
| Segmentation analysis of white matter tracts<br />
| Year 3<br />
| Completed, applications ongoing<br />
|-<br />
| '''Utah'''<br />
| 1.5<br />
| Joint analysis of DTI and functional data<br />
| Year 5<br />
| Initiated<br />
|-<br />
| '''Utah'''<br />
| 2<br />
| Nonparametric Shape Analysis<br />
| Year 5<br />
| Completed<br />
|-<br />
| '''Utah'''<br />
| 2.1<br />
| Framework in place<br />
| Year 3<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.2<br />
| Demonstration on shape of neuranatomy (from Core 3)<br />
| Year 4<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.3<br />
| Development for multiobject complexes<br />
| Year 4<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.4<br />
| Demonstration of NP shape representations on clinical hypotheses from Core 3<br />
| Year 5<br />
| Complete, publications in progress<br />
|-<br />
| '''Utah'''<br />
| 2.6<br />
| Integration into NAMIC-kit<br />
| Year 5<br />
| Incomplete (initiated)<br />
|-<br />
| '''Utah'''<br />
| 2.7<br />
| Shape regression<br />
| Year 5<br />
| Incomplete<br />
|-<br />
<br />
|-<br />
| '''UNC'''<br />
| 1<br />
| '''Statistical shape analysis'''<br />
|<br />
|<br />
|-<br />
| '''UNC'''<br />
| 1.1<br />
| Comparative anal. of shape anal. schemes<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 1.3<br />
| Statistical shape analysis incl. patient variable<br />
| Year 5<br />
| Complete, refinements ongoing<br />
|-<br />
| '''UNC'''<br />
| 2<br />
| '''Structural analysis of DW-MRI'''<br />
|<br />
|<br />
|-<br />
| '''UNC'''<br />
| 2.1<br />
| DTI tractography tools<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 2.2<br />
| Geometric characterization of fiber tracts<br />
| Year 5<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 2.3<br />
| Quant. anal. of diffusion along fiber tracts<br />
| Year 5<br />
| Completed.<br />
|-<br />
| '''GaTech'''<br />
| 1.1<br />
| ITK Implementation of PDEs<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.1<br />
| Applications to Core 3 data<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.2<br />
| New statistic models<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.2<br />
| Shape anaylsis<br />
| Year 4<br />
| Completed, refinements ongoing<br />
|-<br />
| '''GaTech'''<br />
| 2.0<br />
| Integration in to Slicer<br />
| Year 4-5<br />
| Preliminary results and ongoing<br />
|-<br />
| '''MGH'''<br />
| 1<br />
| '''Registration'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 1.1<br />
| Collect DTI/QBALL data<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 1.2<br />
| Develop registration method<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 1.3<br />
| Test/optimize registration method<br />
| Year 3<br />
| In Progress<br />
|-<br />
| '''MGH'''<br />
| 1.4<br />
| Apply registration on core 3 data<br />
| Year 5<br />
| In Queue<br />
|-<br />
| '''MGH'''<br />
| 2<br />
| '''Group DTI Statistics'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 2.1<br />
| Develop group statistic method<br />
| Year 2<br />
| Partially Complete<br />
|-<br />
| '''MGH'''<br />
| 2.2<br />
| Apply on core 3 data<br />
| Year 5<br />
| In Queue<br />
|-<br />
| '''MGH'''<br />
| 3<br />
| '''Diffusion Segmentation '''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 3.1<br />
| Collect DTI/QBALL data<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 3.2<br />
| Develop/optimize segmentation algorithm<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 3.3<br />
| Integrate w/ tractography<br />
| Year 4<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 3.4<br />
| Apply on core 3 data<br />
| Year 5<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4<br />
| '''Group Morphometry Statistics'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 4.1<br />
| Develop/optimize statistics algorithms<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.2<br />
| Develop GUI for Linux<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.3<br />
| Slicer integration<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.4<br />
| Compile application on Windows<br />
| Year 4<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 5<br />
| XNAT Desktop<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 5.1<br />
| Establish requirements for desktop version of XNAT <br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 5.2<br />
| Develop implementation plan for prototype<br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 5.3<br />
| Implement prototype version <br />
| Years 4-5<br />
| Incomplete (in progress)<br />
|-<br />
| '''MGH'''<br />
| 5.4<br />
| Implement alpha version<br />
| Year 5<br />
| Incomplete<br />
|-<br />
| '''MGH'''<br />
| 6<br />
| XNAT Central<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 6.1<br />
| Deploy XNAT Central, a public access XNAT host <br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 6.2<br />
| Coordinate with NAMIC sites to upload project data<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 6.3<br />
| Continue developing XNAT Central based on feedback from NAMIC sites<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 7<br />
| NAMIC Kit integration<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 7.1<br />
| Implement web services to exchange data with Slicer, Batchmake, and other client applications<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 7.2<br />
| Add XNAT Desktop to standard NAMIC kit distribution<br />
| Year 5<br />
| Incomplete<br />
|-<br />
|}<br />
<br />
=== Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| '''MIT'''<br />
| 2.2<br />
| Methods to compare shape statistics<br />
| Removed, the effort refocused on registration necessary for population studies <br />
|-<br />
| '''MIT'''<br />
| 2.4<br />
| Software infrastructure to integrate shape analysis tools into the pipeline for population studies.<br />
| New, morphed into collaboration with XNAT to provide more general population analysis tools. Partially completed.<br />
|-<br />
| '''MIT'''<br />
| 4<br />
| fMRI analysis including local and atlas-based priors for quantifying activation.<br />
| New, partially completed. Refinements in progress. Clinical study with Core 1 is in progress.<br />
|-<br />
| '''Utah'''<br />
| 2.2 (removed)<br />
| Feature-based brain image registration.<br />
| Shift emphasis to shape-based analysis/registration<br />
|-<br />
| '''Utah'''<br />
| 2.1 (removed)<br />
| Cortical filtering and feature detection<br />
| Effort is subsumed by other Core 1 partners (e.g. see MGH/Freesurfer)<br />
|-<br />
| '''Utah'''<br />
| 1.3 (removed)<br />
| Segmentation of cortex/WM<br />
| Effort is subsumed by other Core 1-2 partners (e.g. see EM-Segmenter)<br />
|-<br />
| '''Utah'''<br />
| 3.0 (removed)<br />
| Fast implmentations of PDEs<br />
| Real-time filtering is demphasized in favor of shape/DTI analysis<br />
|-<br />
| '''Utah'''<br />
| 1.5 (added)<br />
| Joint analysis of DTI and functional data<br />
| Opportunities/needs within various collaborations<br />
|-<br />
| '''Utah'''<br />
| 2.1-2.3 (added, in place of cortical analysis)<br />
| Shape analysis<br />
| Nonparametric shape analysis added to address needs of core 3.<br />
|-<br />
| '''Utah'''<br />
| 2.7<br />
| Shape regression<br />
| Extension/completion of framework. Opportunities/needs within various collaborations.<br />
|-<br />
| '''UNC'''<br />
| 1.2<br />
| Develop medially-based shape representation<br />
| Remove<br />
|-<br />
| '''UNC'''<br />
| 1.4<br />
| Develop generic cortical correspondence framework (Years 3-5)<br />
| New<br />
|-<br />
| '''UNC'''<br />
| 2.4<br />
| DTI Atlas Building (Years 2--4)<br />
| New<br />
|-<br />
| '''GaTech'''<br />
| 2.1<br />
| FA analysis<br />
| New<br />
|-<br />
| '''MGH'''<br />
| 4.1 - 4.4 <br />
| Group Morphometry Statistics<br />
| Added and then removed, based on personnel changes<br />
|-<br />
| '''MGH'''<br />
| 5-7 <br />
| XNAT<br />
| Added to support remote image database capabilities<br />
|}<br />
<br />
=== [[Core_1_Timeline_Notes|Core 1 Timeline Notes ]] ===<br />
<br />
== Core 2: Engineering ==<br />
<br />
=== Core 2 Timelines and Milestones ===<br />
<br />
<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''GE'''<br />
| 1<br />
| '''Define software architecture'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Object design<br />
| Yr 1<br />
| Completed<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Identify patterns<br />
| Yr 3<br />
| Patterns for processing scalar and vector images, models, fiducials complete. Patterns for diffusion weighted completed, fMRI ongoing.<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Create frameworks<br />
| Yr 3<br />
| Frameworks for processing scalar and vector images, models, fiducials complete. Frameworks for diffusion weighted completed, fMRI ongoing.<br />
|-<br />
| '''GE'''<br />
| 2<br />
| '''Software engineering process'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Extreme programming<br />
| Yr 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Process automatiion<br />
| Yr 3<br />
| On schedule, ongoing<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Refactoring<br />
| Yr 3<br />
| Complete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| '''Automated quality system'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 3<br />
| DART deployment<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| Persistent testing system<br />
| Yr 5<br />
| Incomplete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| Automatic defect detection<br />
| Yr 5<br />
| Incomplete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| '''Cross-platform development'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Deploy environment (CMake, CTest)<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| DART Integration and testing<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Documentation tools<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| '''Integration tools'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| File Formats/IO facilities<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| CableSWIG deployment<br />
| Yr 3<br />
| Complete (integration ongoing)<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Establish XML schema<br />
| Yr 4<br />
| Complete, refinements ongoing<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| '''Technology delivery'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Deploy applications<br />
| Yr 1<br />
| Complete (ongoing)<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Establish plug-in repository<br />
| Yr 2<br />
| Incomplete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Cpack<br />
| Yr 4-5<br />
| Incomplete<br />
|-<br />
| '''Isomics'''<br />
| 1<br />
| NAMIC builds of slicer<br />
| Years 2--5<br />
| Complete<br />
|-<br />
| '''Isomics'''<br />
| 1<br />
| Schizophrenia and DBP intefaces<br />
| Year 3---5<br />
| Completed (refinements ongoing)<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| ITK Integration tools<br />
| Year 1---3<br />
| Completed<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| Experiment Control Interfaces<br />
| Year 2---5<br />
| Migration from LONI to BatchMake Underway<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| fMRI/DTI algorithm support<br />
| Year 2---5<br />
| Completed DTI, fMRI Ongoing<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| New DBP algorithm support<br />
| Year 2---5<br />
| Ongoing<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Compatible build process<br />
| Year 1---3<br />
| Completed<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Dart Integration<br />
| Year 1---2<br />
| Completed (upgrades ongoing)<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Test scripts for new code<br />
| Year 2---5<br />
| Ongoing<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Grid computing---base<br />
| Year 1<br />
| Completed<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Grid enabled algorithms<br />
| Year 3<br />
| First version (GWiz alpha) available - initial integration with Slicer3 and execution model.<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Testing infrastructure<br />
| Year 4<br />
| Initiated<br />
|-<br />
| '''UCSD'''<br />
| 2<br />
| Data grid --- compatibility<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''UCSD'''<br />
| 2<br />
| Data grid --- slicer access<br />
| Year 2<br />
| Completed for version 2.6. In progress for Slicer3<br />
|-<br />
| '''UCSD'''<br />
| 3<br />
| Data mediation --- deploy<br />
| Year 1<br />
| Incomplete (modfication below)<br />
|-<br />
| '''UCLA'''<br />
| 1<br />
| Debabeler functionality<br />
| Year 1<br />
| Continued Progress<br />
|-<br />
| '''UCLA'''<br />
| 2<br />
| SLIPIE Interpretation (Layer 1)<br />
| Year 1--Year2<br />
| In Progress<br />
|-<br />
| '''UCLA'''<br />
| 3<br />
| SLIPIE Interpretation (Layer 2)<br />
| Year 1--Year2<br />
| On Schedule<br />
|-<br />
| '''UCLA'''<br />
| 3<br />
| Developing ITK Modules<br />
| Year2<br />
| In Progress<br />
|-<br />
| '''UCLA'''<br />
| 4<br />
| Integrating SRB (GSI-enabled)<br />
| Year2<br />
| Completed<br />
|-<br />
| '''UCLA'''<br />
| 5<br />
| Integrating IDA<br />
| Year2<br />
| Completed<br />
|-<br />
| '''UCLA'''<br />
| 5<br />
| Integrating External Visualization Applications<br />
| Year2<br />
| Completed<br />
|}<br />
<br />
=== Core 2 Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Data mediation<br />
| Delayed pending integration of databases into NAMIC infractructure<br />
|}<br />
<br />
=== [[Core_2_Timeline_Notes|Core 2 Timeline Notes ]] ===<br />
<br />
== Core 3: Driving Biological Problems ==<br />
<br />
The Core 3 projects submitted R01 style proposals, as specified in the RFA, and did not submit timelines.<br />
<br />
== Core 4: Service ==<br />
<br />
=== Core 4 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| '''Implement Development Farms'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Deploy platforms<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Communications<br />
| Yrs 1<br />
| Complete, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| '''Establish software process'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Secure developer database<br />
| Yr 1<br />
| Complete, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Collect guidelines<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Manage software submission process<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Configure process tools<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Survey community<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| '''Deploy NAMIC Tools'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Toolkits<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Integration tools<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Applications<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Integrate new computing resources<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| '''Provide support'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| Esablish support infrastructure<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| NAMIC support<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 5<br />
| Manage NAMIC Software Releases<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|}<br />
<br />
=== Core 4 Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| Kitware<br />
| 2-5<br />
| Various<br />
| Refined/modified the sub aims<br />
|}<br />
<br />
=== [[Core_4_Timeline_Notes|Core 4 Timeline Notes ]] ===<br />
<br />
== Core 5: Training ==<br />
<br />
=== Core 5 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| '''Formal Training Guidllines'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| Functional neuroanatomy<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| Clinical correlations<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| '''Mentoring'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| Programming workshops<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| One-on-one mentoring, Cores 1, 2, 3<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| '''Collaborative work environment'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Wiki<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Mailing lists<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Regular telephone conferences<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 4<br />
| '''Educational component for tools'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 4<br />
| Slicer training modules<br />
| Yr 2-5<br />
| Slicer 2.x tutorials complete, Two Slicer 3 tutorials complete, translation of 2.x tutorials to 3 is ongoing and on schedule<br />
|-<br />
| '''Harvard'''<br />
| 5<br />
| '''Demonstrations and hands-on training'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 5<br />
| Various workshops and conferences<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|}<br />
<br />
=== Core 5 Timeline Modifications ===<br />
<br />
None.<br />
<br />
=== [[Core_5_Timeline_Notes|Core 5 Timeline Notes ]] ===<br />
<br />
== Core 6: Dissemination ==<br />
<br />
=== Core 6 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| Isomics<br />
| 1<br />
| Create a collaboration metholdology for NA-MIC<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 1.1<br />
| develop a selection process<br />
| Yr 1<br />
| Complete<br />
|-<br />
| Isomics<br />
| 1.2<br />
| guidelines to govern the collaborations<br />
| Yr 1-2<br />
| Complete<br />
|-<br />
| Isomics<br />
| 1.3<br />
| Provide on-site training<br />
| Yr 1-5<br />
| Complete for current tools (ongoing for tool refinement)<br />
|-<br />
| Isomics<br />
| 1.4<br />
| develop a web site infrastructure<br />
| Yr 1<br />
| Complete<br />
|-<br />
| Isomics<br />
| 2<br />
| Facilitate communication between NA-MIC developers and wider research community<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 2.1<br />
| develop materials describing NAMIC technology<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.2<br />
| participate in scientific meetings<br />
| Yr 2-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.3<br />
| Document interactions with external researchers<br />
| Yr 2-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.4<br />
| Coordinate publication strategies<br />
| Yr 3-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3<br />
| Develop a publicly accessible internet resource of data, software, documentation, and publication of new discoveries<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 3.1<br />
| On-line repository of NAMIC related publications and presentations<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3.2<br />
| On-line repository of NAMIC tutorial and training material<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3.3<br />
| Index and a searchable database<br />
| Yr 1-2<br />
| Done<br />
|-<br />
| Isomics<br />
| 3.4<br />
| Automated feedback systems that track software downloads<br />
| Yr 3<br />
| Done<br />
|}<br />
<br />
=== Core 6 Timeline Modifications ===<br />
<br />
None.<br />
<br />
=== [[Core_6_Timeline_Notes|Core 6 Timeline Notes ]] ===<br />
<br />
=Appendix A Publications (Mastrogiacomo)=<br />
A list should be mined from the publications database and attached here in MS word format.<br />
<br />
=Appendix B EAB Report and Response (Kapur)=<br />
===EAB Report===<br />
<br />
===Response to EAB Report===</div>Kubickihttps://www.na-mic.org/w/index.php?title=2009_Annual_Scientific_Report&diff=362912009 Annual Scientific Report2009-04-15T17:54:40Z<p>Kubicki: /* Engineering Component (Davis) */</p>
<hr />
<div>Back to [[2009_Progress_Report]]<br />
<br />
<br />
<br />
<br />
<br />
=Guidelines for preparation=<br />
<br />
*[[2009_Progress_Report#Scientific Report Timeline]] - Main point is that May 15 is the date by which all sections below need to be completed. No extensions are possible.<br />
*DBPs - If there is work outside of the roadmap projects that you would like to report, you are welcome to create a separate section for it under "Other". <br />
*The outline for this report is similar to the 2008 and 2007 reports, which are provided here for reference: [[2008_Annual_Scientific_Report]], [[2007_Annual_Scientific_Report]].<br />
*In preparing summaries for each of the 8 topics in this report, please leverage the detailed pages for projects provided here: [[NA-MIC_Internal_Collaborations]].<br />
*Publications will be mined from the SPL publications database. All core PIs need to ensure that all NA-MIC publications are in the publications database by May 15.<br />
<br />
=Introduction (Tannenbaum)=<br />
<br />
The National Alliance for Medical Imaging Computing (NA-MIC) is now in its fifth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. This was our second year with our current DBPS of which three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. The and fourth is a very new direction, the prostate: brachytherapy needle positioning robot integration.<br />
<br />
We briefly summarize the work of NAMIC during the five years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main themes: Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. The third year of center activity was devoted to the continuation of the collaborative efforts in order to give solutions to the various brain-oriented DBPs. The fourth year was focused on translating our work to the new DBPs. In the fifth year, a number of projects reached the point where modules were introduced<br />
into Slicer making the Core 1 algorithms available to the general medical imaging community. A number of the algorithms are quite general and can be used for purposes much broader than the original DBPs. For example, a new point cloud registration algorithm was developed for the prostate brachytherapy needle positioning project can be used also for DWI registration.<br />
Work on DTI/DWI tractography has impacted the segmentation of blood vessels and soft plaque detection in the coronaries.<br />
<br />
Year five has seen progress with the work of our current DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.<br />
<br />
In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent work on the four infrastructure topics. These include: Diffusion Image analysis (Section 4.1), Structural analysis (Section 4.2), Functional MRI analysis (Section 4.3), and the NA-MIC Toolkit (Section 4.4). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final sections of this report, Sections 5-11, provide updated timelines on the status of the various projects of the different cores of NAMIC.<br />
<br />
=Clinical Roadmap Projects=<br />
==Roadmap Project: Stochastic Tractography for VCFS (Kubicki)==<br />
===Overview (Kubicki)===<br />
The goal of this project is to create an end-to-end application that would be useful in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-facial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.<br />
<br />
===Algorithm Component (Golland)===<br />
At the core of this project is the stochastic tractography algorithm<br />
developed and implemented in collaboration between MIT and<br />
BWH. Stochastic Tractography is a Bayesian approach to estimating<br />
nerve fiber tracts from DTI images.<br />
<br />
We first use the diffusion tensor at each voxel in the volume to<br />
construct a local probability distribution for the fiber direction<br />
around the principal direction of diffusion. We then sample the tracts<br />
between two user-selected ROIs, by simulating a random walk between<br />
the regions, based the local transition probabilities inferred from<br />
the DTI image.<br />
<br />
The resulting collection of fibers and the associated FA values<br />
provide useful statistics on the properties of connections between the<br />
two regions. To constrain the sampling process to the relevant white<br />
matter region, we use atlas-based segmentation to label ventricles and<br />
gray matter and to exclude them from the search space. As such, this<br />
step relies heavily on the registration and segmentation functionality<br />
in Slicer.<br />
<br />
Over the last year, we have been working on applying several pre- and postprocessing steps to the algorithm pipeline. These steps include eddy current and geometric distortion correction that have been made available to us by Utah group, as well as DTI filtering (BWH). White matter masks can also now be created based on T2 thresholding within the slicer stochastic tractography module, which makes them more precise, since they do not rely on MRI to DTI co-registration. <br />
<br />
At the same time we are working on the datasets where fMRI activations as well as gray matter segmentations need to be registered to DTI data, in order to seed within the predefined gray matter regions. We have made a significant progress in between modality registration, additional improvement is expected when distortion correction become part of the analysis pipeline. <br />
<br />
We are also working on improved ways to visualize and quantify stochastic tractography output, not only by parametrizing fiber tracts, but also by creating connection probability distribution maps.<br />
<br />
===Engineering Component (Davis)===<br />
Stochastic Tractography slicer module has been rewritten in python now, and new module released in December 2008, and presented at the AHM in SLC. Its now part of the slicer3. Module documentation have been also created. Current engineering efforts are concentrated on maintaining the module, optimizing it for working with other data formats, and adding new functionality, such as better registration, distortion correction and ways of extracting and measuring FA along the tracts. <br />
<br />
Also, because of the fact that the new data is much more computationally demanding (higher spatial resolution, more diffusion directions), and cortical ROIs usually much larger than the previously used WM ROIs, there is general need for performance improvement. This issue is highlighted especially by our stochastic way of tracking connections, where hundreds, instead of just one, (as in deterministic tractography) tracts are being generated from one seed. Thus some of our efforts go towards multithreading, and utilizing parallel processing. Version of our algorithm that uses large computer clusters have been developed and can be downloaded and installed by individual users with minimal knowledge of parallel computing now.<br />
<br />
===Clinical Component (Kubicki)===<br />
<Note Progress in the last year><br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard:Brain_Segmentation_Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==<br />
===Overview (Fichtinger)===<br />
Numerous studies have demonstrated the efficacy of image-guided<br />
needle-based therapy and biopsy in the management of prostate<br />
cancer. The accuracy of traditional prostate interventions performed using<br />
transrectal ultrasound (TRUS) is limited by image fidelity, needle<br />
template guides, needle deflection and tissue deformation. Magnetic Resonance<br />
Imaging (MRI) is an ideal modality for guiding and monitoring<br />
such interventions due to its excellent visualization of the prostate, its<br />
sub-structure and surrounding tissues. <br />
<br />
We have designed a comprehensive robotic assistant system that allows prostate biopsy and brachytherapy procedures to be performed entirely inside a 3T closed MRI scanner. The current system applies transrectal approach to the prostate: an endorectal coil and steerable needle guide, both tuned to 3T magnets and invariable to any particular scanner, are integrated into the MRI compatible manipulator.<br />
<br />
Under the NAMIC initiative, the image computing, visualization, intervention planning, and kinematic planning interface is being accomplished with open source system built on the NAMIC toolkit and its components, such as Slicer3 and ITK. These are complemented by a collection of unsupervised prostate segmentation and registration methods that are of great importance to the clinical performance of the interventional system as a whole.<br />
<br />
===Algorithm Component (Tannenbaum)===<br />
<br />
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps below.<br />
<br />
'''Prostate Segmentation'''<br />
<br />
We must first extract the prostate. We provided two methods: a shape based method and a semi-automatic method. More details are given below and images and further details may be found [http://www.na-mic.org/Wiki/index.php/Projects:ProstateSegmentation here]<br />
<br />
# ''A shape based algorithm''. This begins with learning a group of shapes, obtained from manually segmenting a set of prostate 3D images. With the shapes represented as the hyperbolic tangent of the signed distance functions, principle component analysis is employed to learn the shapes. Further, given a new prostate image, we search the learned shape space in order to find one shape best segment the given image. The fitness of one shape to segment the image is evaluated by an energy functional measuring the discrepancy of the statistical characteristics inside and outside the current segmentation boundary. Such method is robust to the noise in the images. Moreover, the whole algorithm pipeline has been integrated into the Slicer3 through the command line module.<br />
# ''Semi-automatic method''. This method is based on a random walk segmentation algorithm. With user provided initial seed regions inside and out side the object (prostate), the algorithm computes a probability distribution over the image domain by solving a boundary value partial differential equation where the value at seed regions are fixed at 1.0 or 0.0, depending or whether they are object or background seeds. The resulting distribution indicates the probability of each voxel belonging to the object. Simply threshold by 0.5 gives the segmentation of the object. Moreover, if the result is not suitable, the user can edit the seed regions, and the new result is computed based on this previous result. This algorithm has been integrated into the transrectal prostate MRI module of Slier3.<br />
<br />
'''Prostate Registration'''<br />
<br />
We developed a nonlinear (affine) prostate registration method by treating prostate images as point sets. Then the iterative closest point algorithm is improved to register the point sets generated by the two images to be registered. The proposed method shows robustness to long distance transition and partial image structure. Moreover, such representation is much sparser than sampling image on the uniform grid thus the registration is very fast comparing two 3D volumetric<br />
image registration.<br />
<br />
Furthermore, the registration is viewed as a posterior estimation problem, in which the distributions of the affine and translation parameters are to be estimated. This can naturally be estimated using a particle filter framework. Through this, the method can handle the otherwise difficult cases where the two prostates are one supine and<br />
one prone.<br />
<br />
More details are given [[Projects:pfPtSetImgReg|here...]]<br />
<br />
===Engineering Component (Hayes)===<br />
<Note Progress in the last year><br />
<br />
<br />
===Clinical Component (Fichtinger)===<br />
<Note Progress in the last year><br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:JHU:Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus (Bockholt)==<br />
===Overview (Bockholt)===<br />
The primary goal of the MIND DPB is to examine changes in white matter lesions in adults with Neuropsychiatric Systemic Lupus Erythematosus (SLE). We want to be able to characterize lesion location, size, and intensity, and would also like to examine longitudinal changes of lesions in an SLE cohort. To accomplish this goal, we will create an end-to-end application entirely within NA-MIC Kit allowing individual analysis of white matter lesions. Such a workflow will then be applied to a clinical sample in the process of being collected.<br />
<br />
===Algorithm Component (Whitaker)===<br />
The basic steps necessary for the white matter lesion analysis application entail first registration of T1, T2, and FLAIR images, second tissue classification into gray, white, csf, or lesion, thirdly clustering lesion for anatomical localization, and finally a summarization of lesion size and image intensity parameters within each unique lesion. <br />
<br />
<Note Progress in the last year><br />
<br />
===Engineering Component (Pieper)===<br />
<Note Progress in the last year><br />
<br />
===Clinical Component (Bockholt)===<br />
<Note Progress in the last year><br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND:Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Cortical Thickness for Autism(Hazlett)== <br />
===Overview (Hazlett)===<br />
<br />
A primary goal of the UNC DPB is to examine changes in cortical thicknes in children with autism compared to typical controls. We want to examine group differences in both local and regional cortical thickness, and would also like to examine longitudinal changes in the cortex from ages 2-4 years. To accomplish this goal, this project will create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness. Such a workflow will then be applied to our study data (already collected).<br />
<br />
===Algorithm Component (Styner)===<br />
<br />
The basic steps necessary for the cortical thickness application entail first tissue segmentation in order to separate white and gray matter regions, second cortical thickness measurement, thirdly cortical correspondence to compare measurements across subjects and finally a statistical analysis to locally compute group differences.<br />
<br />
<Note Progress in the last year><br />
<br />
===Engineering Component (Miller, Vachet)===<br />
<br />
<Note Progress in the last year><br />
<br />
===Clinical Component (Hazlett)===<br />
<Note Progress in the last year><br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNC:Cortical_Thickness_Roadmap here on the NA-MIC wiki].<br />
<br />
=Four Infrastructure Topics=<br />
==Diffusion Image Analysis (Gerig)==<br />
<Note Progress in the last year><br />
===Key Investigators===<br />
<br />
<Need to update the list below><br />
<br />
* BWH: Marek Kubicki, Martha Shenton, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Carl-Fredrik Westin, Gordon Kindlmann<br />
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo<br />
* UCI: James Fallon<br />
* Utah I: Tom Fletcher, Ross Whitaker, Ran Tao, Yongsheng Pan<br />
* Utah II: Casey Goodlett, Sylvain Gouttard, Guido Gerig<br />
* GA Tech: John Melonakos, Vandana Mohan, Shawn Lankton, Allen Tannenbaum<br />
* GE: Xiaodong Tao, Jim Miller<br />
* Isomics: Steve Pieper<br />
* Kitware: Luis Ibanez<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:DiffusionImageAnalysis here on the NA-MIC wiki].<br />
<br />
==Structural Analysis(Tannenbaum)==<br />
===Progress===<br />
Under Structural Analysis, the main topics of research for NAMIC are structural segmentation, registration techniques and shape analysis. These topics are correlated and hence research in one often finds application in another. For example, shape analysis can yield useful priors for segmentation, or segmentation and registration can provide structural correspondences for use in shape analysis and so on. <br />
<br />
An overview of selected progress highlights under these broad topics follows:<br />
<br />
Segmentation<br />
<br />
* Geodesic Tractography Segmentation: We proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. This has been applied successfully to the segmentation of neural fiber bundles such as the Cingulum Bundle. This framework has now been integrated into Slicer and is being tested on a population of brain data sets.<br />
<br />
* Tubular Surface Segmentation: We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels. <br />
<br />
* Local-global Segmentation: We have proposed a novel segmentation approach that combines the advantages of local and global approaches to segmentation, by using statistics over regions that are local to each point on the evolving countour. This makes it well suited to applications with contrast differences within the structure of interest such as in blood vessel segmentation, as well as applications like the neural fiber bundles where the diffusion profiles of voxels within the structure are locally similar but vary along the length of the fiber bundle itself.<br />
<br />
* Shape-based segmentation: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have presented an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. We have applied the algorithm successfully to real MRI images, and we have also implemented it into 3D Slicer.<br />
<br />
* Re-Orientation Approach for Segmentation of DW-MRI: This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation which allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares very favorably with segmentation by full-brain streamline tractography. <br />
<br />
<br />
Registration<br />
<br />
* Optimal Mass Transport based Registration: We have provided a computationaly efficient non-rigid/elastic image registration algorithm based on the Optimal Mass Transport theory. We use the Monge-Kantorovich formulation of the Optimal Mass Transport problem and implement the solution proposed by Haker et al. using multi-resolution and multigrid techniques to speed up the convergence. We also leverage the computation power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We extend the work by Haker et al. who compute the optimal warp from a first order partial differential equation, an improvement over earlier proposed higher order methods and those based on linear programming, and further implement the algorithm using a coarse-to-fine strategy resulting in phenomenol improvement in convergence. We have applied it successfully to the registration of 3D brain MRI datasets (preoperative and intra-operative), and are currently extending it to the non-rigid registration of baseline DWI to brain MRI data.<br />
<br />
* Atlas Regularization for Image Segmentation: Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. <br />
<br />
* Point-set Rigid Registration: We have proposed a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation. Moreover, we incorporate stochastic dynamics to model the uncertainity of the registration process. We treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temoral coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets.We applied the algorithm to different alignments of point clouds and it successfully found the correct optimal transformation that aligns two given point clouds despite the differing geometry around the local neighborhood of a point within their respective sets. <br />
<br />
* Regularization for Optimal Mass Transport: To extend the flexibility of the existing OMT algorithm, we added a regularization term to the functional being minimized. This term controls the tradeoff between how well two images match after registration versus how warped the transformation map can become. A weighted sum of squared differences is used to penalize having to move mass over long distances; this addition also helps to keep the transformation physically accurate by reducing the likelihood that the transformation grid will fold over itself and keeping the grid smooth.<br />
<br />
* Registration of DW-MRI to structural MRI: Optimal Mass Transport was applied to the problem of correcting EPI distortion in DW-MRI. A mask for white matter in DW-MRI was registered to the white matter mask extracted from the structural MRI for the same patient. Prior to registration, it is important to normalize intensities in the two masks; this was done by dividing the images into regions and uniformly normalizing over each region to assure the sum of the intensities is equal. Then, once a transformation between the white matter masks was calculated, this transformation was applied to the original DW-MRI image. <br />
<br />
Shape Analysis<br />
<br />
* Shape Analysis Framework using SPHARM-PDM: We have provided an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are first processed to fill any interior holes. The processed binary segmentations are converted to surface meshes, and a spherical parametrization is computed for the surface meshes using a area-preserving, distortion minimizing spherical mapping. The SPHARM description is computed from the mesh and its spherical parametrization. Using the first order ellipsoid from the spherical harmonic coefficients, the spherical parametrizations are aligned to establish correspondence across all surfaces. The SPHARM description is then sampled into a triangulated surfaces (SPHARM-PDM) via icosahedron subdivision of the spherical parametrization. These SPHARM-PDM surfaces are all spatially aligned using rigid Procrustes alignment. Group differences between groups of surfaces are computed using the standard robust Hotelling T 2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. We provide additional visualization of the group tests via mean difference magnitude and vector maps, as well as maps of the group covariance information. We have a stable implementation, and current development focuses on integrating the current command line tools into Slicer (v3) via the Slicer execution model. <br />
<br />
* Population studies using Tubular Surface Model: We have proposed a tubular shape model for the Cingulum Bundle which models a tubular surface as a center-line coupled with a radius function at every point along the center-line. This model shows potential for population studies on the Cingulum Bundle which is believed to be involved in Schizophrenia, since it provides a natural way of sampling the structure to build a feature representation of it. We are currently segmenting the Cingulum Bundle from a population of brain data sets, towards performing this population analysis using the Pott's Model.<br />
<br />
* Automatic Outlining of Sulci on a Brain Surface: We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain, where the data is taken to be a 3D triangulated mesh formed from the segmentation of MR image slices. The problem is posed as energy minimization using penalizing the arc-length of segmenting curve using conformal factor involving the mean curvature of the underlying surface. The computation is made practical for dense meshes via the use of a sparse-field method to track the level set interfaces and regularized least-squares estimation of geometric quantities.<br />
<br />
===Key Investigators===<br />
<br />
Needs to be updated:<br />
<br />
* MIT: Polina Golland, Kilian Pohl, Sandy Wells, Eric Grimson, Mert R. Sabuncu<br />
* UNC: Martin Styner, Ipek Oguz, Xavier Barbero <br />
* Utah: Ross Whitaker, Guido Gerig, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer <br />
* GaTech: Allen Tannenbaum, John Melonakos, Vandana Mohan, Tauseef ur Rehman, Shawn Lankton, Samuel Dambreville, Yi Gao, Romeil Sandhu, Xavier Le Faucheur, James Malcolm, Ivan Kolosev<br />
* Isomics: Steve Pieper <br />
* GE: Bill Lorensen, Jim Miller <br />
* Kitware: Luis Ibanez, Karthik Krishnan<br />
* UCLA: Arthur Toga, Michael J. Pan, Jagadeeswaran Rajendiran <br />
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt, Yogesh Rathi, Marek Kubicki, Steven Haker<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:StructuralImageAnalysis here on the NA-MIC wiki].<br />
==fMRI Analysis (Golland)==<br />
===Progress===<br />
One of the major goals in analysis of fMRI data is the detection of<br />
functionally homogeneous networks in the brain. <br />
<br />
<note progress here><br />
<br />
===Key Investigators===<br />
<br />
Need to update this list:<br />
<br />
# MIT: Polina Golland, Danial Lashkari, Bryce Kim <br />
# Harvard/BWH: Sylvain Bouix, Martha Shenton, Marek Kubicki<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:fMRIAnalysis here on the NA-MIC wiki].<br />
==NA-MIC Kit Theme (Schroeder)==<br />
===Progress===<br />
The NAMIC-Kit consists of a framework of advanced computational components, as well as the support infrastructure for testing, documenting, and deploying leading edge medical imaging algorithms and software tools. The framework has been carefully constructed to provide low-level access to libraries and modules for advanced users, plus high-level application access that non-computer professionals can use to address a variety of problems in biomedical computing. In this fifth year of the NA-MIC projects <summary of progress><br />
<br />
===Software Releases===<br />
The NAMIC-Kit can be represented as a pyramid of capabilities, with the base consisting of toolkits and libraries, and the apex standing in for the Slicer3 user application. In between, Slicer modules are stand-alone executables that can be integrated directly into the Slicer3 application, including GUI integration, while work-flows are groups of modules that are integrated together to manifest sophisticated segmentation, registration and biomedical computing algorithms. In a coordinated NAMIC effort, major releases of these many components were realized over the past year. This includes, but is not limited to:<br />
*<br />
*<br />
<br />
===Slicer3 and the Software Framework===<br />
One of the major achievements of the past year has been...<br />
<br />
===Software Process===<br />
One of the challenges facing developers has been the requirement to implement, test and deploy software systems across multiple computing platforms. NAMIC continues to push the state of the art with further development of the CMake, CTest, and CPack tools for cross-platform development, testing, and packaging, respectively...<br />
<br />
===Key Investigators===<br />
THis list needs to be updated:<br />
<br />
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman<br />
* GE - Jim Miller, Xiaodong Tao<br />
* Isomics - Steve Pieper<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC-Kit here on the NA-MIC wiki].<br />
<br />
<br />
<br />
=Highlights(Schroeder)=<br />
===Advanced Algorithms===<br />
<br />
===NAMIC-Kit===<br />
<br />
===Outreach and Technology Transfer===<br />
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.<br />
* The Slicer community held several workshops and tutorials. In xxx a satellite event was held for the international Organization for Human Brain Mapping at the annual meeting in xxx. The xx workshop on xx hosted xx participants representing xx countries from around the world, xx states within the US and xxdifferent laboratories including xx NIH institutes. In addition, <note how many slicer tutorials were held and where etc><br />
* Project Week continues to be a successful NAMIC venue. These semi-annual events are held in Boston in June, and January in Salt Lake City. These events are well attended with approximately 100 participants, of which about a third are outside collaborators. At the last Project Week in Salt Lake City, approximately xx projects were realized.<br />
* NAMIC continues to participate in conferences and other technical venues. For example, NAMIC hosted xxx<br />
<br />
<br />
=Impact and Value to Biocomputing (Miller)=<br />
NA-MIC impacts Biocomputing through a variety of mechanisms. First,<br />
NA-MIC produces scientific results, methodologies, workflows,<br />
algorithms, imaging platforms, and software engineering tools and<br />
paradigms in an open enviroment that contributes directly to the body of<br />
knowledge available to the field. Second, NA-MIC science and<br />
technology enables the entire medical imaging community to build on<br />
NA-MIC results, methods, and techniques, to concentrate on the new<br />
science instead of developing supporting infrastructure, to leverage<br />
NA-MIC scientists and engineers to adapt NA-MIC technology to new<br />
problem domains, and to leverage NA-MIC infrastructure to distribute<br />
their own technology to a larger community.<br />
<br />
===Impact within the Center===<br />
<br />
===Impact within NIH Funded Research===<br />
<br />
===National and International Impact===<br />
<br />
<br />
= Timeline (Ross)=<br />
<br />
<The table needs to be updated><br />
<br />
<br />
This section of the report gives the milestones for years 1 through 5 that are associated with the timelines in the original proposal. We have organized the milestones by core. For each milestone we have indicated the proposed year of completion and a very brief description of the current status. In some cases the milestones include ongoing work, and we have try to indicate that in the status. We have also included tables that list any significant changes to the proposed timelines. On the wiki page, we have links to the notes from the various PIs that give more details on their progress and the status of the milestones.<br />
<br />
'''These tables demonstrate that the project is, on the whole, proceeding according to the originally planned schedule.'''<br />
<br />
<br />
<br />
== Core 1: Algorithms ==<br />
<br />
=== Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''MIT'''<br />
| 1<br />
| '''Shape-based segmentation'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 1.1<br />
| Methods to learn shape representations<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 1.2<br />
| Shape in atlas-driven segmentation<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 1.3<br />
| Validate and refine approach<br />
| Year 5<br />
| In Progress<br />
|-<br />
| '''MIT'''<br />
| 2<br />
| '''Shape analysis'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 2.1<br />
| Methods to compute statistics of shapes<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 2.3<br />
| Validation of shape methods on application data<br />
| Year 5<br />
| Completed, refinements ongoing<br />
|-<br />
| '''MIT'''<br />
| 3<br />
| '''Analysis of DTI data'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 3.1<br />
| Fiber geometry<br />
| Year 3<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 3.2<br />
| Fiber statistics<br />
| Year 5<br />
| Completed, new developments ongoing<br />
|-<br />
| '''MIT'''<br />
| 3.3<br />
| Validation on real data<br />
| Year 5<br />
| Completed, refinements ongoing<br />
|-<br />
| '''Utah'''<br />
| 1<br />
| '''Processing of DTI data'''<br />
|<br />
|<br />
|-<br />
| '''Utah'''<br />
| 1.1<br />
| Filtering of DTI<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''Utah'''<br />
| 1.2<br />
| Quantitative analysis of DTI<br />
| Year 3<br />
| Completed, refinements ongoing<br />
|-<br />
| '''Utah'''<br />
| 1.3<br />
| Segmentation of cortex/WM<br />
| Year 3<br />
| Completed partially, modified below<br />
|-<br />
| '''Utah'''<br />
| 1.4<br />
| Segmentation analysis of white matter tracts<br />
| Year 3<br />
| Completed, applications ongoing<br />
|-<br />
| '''Utah'''<br />
| 1.5<br />
| Joint analysis of DTI and functional data<br />
| Year 5<br />
| Initiated<br />
|-<br />
| '''Utah'''<br />
| 2<br />
| Nonparametric Shape Analysis<br />
| Year 5<br />
| Completed<br />
|-<br />
| '''Utah'''<br />
| 2.1<br />
| Framework in place<br />
| Year 3<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.2<br />
| Demonstration on shape of neuranatomy (from Core 3)<br />
| Year 4<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.3<br />
| Development for multiobject complexes<br />
| Year 4<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.4<br />
| Demonstration of NP shape representations on clinical hypotheses from Core 3<br />
| Year 5<br />
| Complete, publications in progress<br />
|-<br />
| '''Utah'''<br />
| 2.6<br />
| Integration into NAMIC-kit<br />
| Year 5<br />
| Incomplete (initiated)<br />
|-<br />
| '''Utah'''<br />
| 2.7<br />
| Shape regression<br />
| Year 5<br />
| Incomplete<br />
|-<br />
<br />
|-<br />
| '''UNC'''<br />
| 1<br />
| '''Statistical shape analysis'''<br />
|<br />
|<br />
|-<br />
| '''UNC'''<br />
| 1.1<br />
| Comparative anal. of shape anal. schemes<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 1.3<br />
| Statistical shape analysis incl. patient variable<br />
| Year 5<br />
| Complete, refinements ongoing<br />
|-<br />
| '''UNC'''<br />
| 2<br />
| '''Structural analysis of DW-MRI'''<br />
|<br />
|<br />
|-<br />
| '''UNC'''<br />
| 2.1<br />
| DTI tractography tools<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 2.2<br />
| Geometric characterization of fiber tracts<br />
| Year 5<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 2.3<br />
| Quant. anal. of diffusion along fiber tracts<br />
| Year 5<br />
| Completed.<br />
|-<br />
| '''GaTech'''<br />
| 1.1<br />
| ITK Implementation of PDEs<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.1<br />
| Applications to Core 3 data<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.2<br />
| New statistic models<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.2<br />
| Shape anaylsis<br />
| Year 4<br />
| Completed, refinements ongoing<br />
|-<br />
| '''GaTech'''<br />
| 2.0<br />
| Integration in to Slicer<br />
| Year 4-5<br />
| Preliminary results and ongoing<br />
|-<br />
| '''MGH'''<br />
| 1<br />
| '''Registration'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 1.1<br />
| Collect DTI/QBALL data<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 1.2<br />
| Develop registration method<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 1.3<br />
| Test/optimize registration method<br />
| Year 3<br />
| In Progress<br />
|-<br />
| '''MGH'''<br />
| 1.4<br />
| Apply registration on core 3 data<br />
| Year 5<br />
| In Queue<br />
|-<br />
| '''MGH'''<br />
| 2<br />
| '''Group DTI Statistics'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 2.1<br />
| Develop group statistic method<br />
| Year 2<br />
| Partially Complete<br />
|-<br />
| '''MGH'''<br />
| 2.2<br />
| Apply on core 3 data<br />
| Year 5<br />
| In Queue<br />
|-<br />
| '''MGH'''<br />
| 3<br />
| '''Diffusion Segmentation '''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 3.1<br />
| Collect DTI/QBALL data<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 3.2<br />
| Develop/optimize segmentation algorithm<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 3.3<br />
| Integrate w/ tractography<br />
| Year 4<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 3.4<br />
| Apply on core 3 data<br />
| Year 5<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4<br />
| '''Group Morphometry Statistics'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 4.1<br />
| Develop/optimize statistics algorithms<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.2<br />
| Develop GUI for Linux<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.3<br />
| Slicer integration<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.4<br />
| Compile application on Windows<br />
| Year 4<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 5<br />
| XNAT Desktop<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 5.1<br />
| Establish requirements for desktop version of XNAT <br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 5.2<br />
| Develop implementation plan for prototype<br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 5.3<br />
| Implement prototype version <br />
| Years 4-5<br />
| Incomplete (in progress)<br />
|-<br />
| '''MGH'''<br />
| 5.4<br />
| Implement alpha version<br />
| Year 5<br />
| Incomplete<br />
|-<br />
| '''MGH'''<br />
| 6<br />
| XNAT Central<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 6.1<br />
| Deploy XNAT Central, a public access XNAT host <br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 6.2<br />
| Coordinate with NAMIC sites to upload project data<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 6.3<br />
| Continue developing XNAT Central based on feedback from NAMIC sites<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 7<br />
| NAMIC Kit integration<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 7.1<br />
| Implement web services to exchange data with Slicer, Batchmake, and other client applications<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 7.2<br />
| Add XNAT Desktop to standard NAMIC kit distribution<br />
| Year 5<br />
| Incomplete<br />
|-<br />
|}<br />
<br />
=== Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| '''MIT'''<br />
| 2.2<br />
| Methods to compare shape statistics<br />
| Removed, the effort refocused on registration necessary for population studies <br />
|-<br />
| '''MIT'''<br />
| 2.4<br />
| Software infrastructure to integrate shape analysis tools into the pipeline for population studies.<br />
| New, morphed into collaboration with XNAT to provide more general population analysis tools. Partially completed.<br />
|-<br />
| '''MIT'''<br />
| 4<br />
| fMRI analysis including local and atlas-based priors for quantifying activation.<br />
| New, partially completed. Refinements in progress. Clinical study with Core 1 is in progress.<br />
|-<br />
| '''Utah'''<br />
| 2.2 (removed)<br />
| Feature-based brain image registration.<br />
| Shift emphasis to shape-based analysis/registration<br />
|-<br />
| '''Utah'''<br />
| 2.1 (removed)<br />
| Cortical filtering and feature detection<br />
| Effort is subsumed by other Core 1 partners (e.g. see MGH/Freesurfer)<br />
|-<br />
| '''Utah'''<br />
| 1.3 (removed)<br />
| Segmentation of cortex/WM<br />
| Effort is subsumed by other Core 1-2 partners (e.g. see EM-Segmenter)<br />
|-<br />
| '''Utah'''<br />
| 3.0 (removed)<br />
| Fast implmentations of PDEs<br />
| Real-time filtering is demphasized in favor of shape/DTI analysis<br />
|-<br />
| '''Utah'''<br />
| 1.5 (added)<br />
| Joint analysis of DTI and functional data<br />
| Opportunities/needs within various collaborations<br />
|-<br />
| '''Utah'''<br />
| 2.1-2.3 (added, in place of cortical analysis)<br />
| Shape analysis<br />
| Nonparametric shape analysis added to address needs of core 3.<br />
|-<br />
| '''Utah'''<br />
| 2.7<br />
| Shape regression<br />
| Extension/completion of framework. Opportunities/needs within various collaborations.<br />
|-<br />
| '''UNC'''<br />
| 1.2<br />
| Develop medially-based shape representation<br />
| Remove<br />
|-<br />
| '''UNC'''<br />
| 1.4<br />
| Develop generic cortical correspondence framework (Years 3-5)<br />
| New<br />
|-<br />
| '''UNC'''<br />
| 2.4<br />
| DTI Atlas Building (Years 2--4)<br />
| New<br />
|-<br />
| '''GaTech'''<br />
| 2.1<br />
| FA analysis<br />
| New<br />
|-<br />
| '''MGH'''<br />
| 4.1 - 4.4 <br />
| Group Morphometry Statistics<br />
| Added and then removed, based on personnel changes<br />
|-<br />
| '''MGH'''<br />
| 5-7 <br />
| XNAT<br />
| Added to support remote image database capabilities<br />
|}<br />
<br />
=== [[Core_1_Timeline_Notes|Core 1 Timeline Notes ]] ===<br />
<br />
== Core 2: Engineering ==<br />
<br />
=== Core 2 Timelines and Milestones ===<br />
<br />
<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''GE'''<br />
| 1<br />
| '''Define software architecture'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Object design<br />
| Yr 1<br />
| Completed<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Identify patterns<br />
| Yr 3<br />
| Patterns for processing scalar and vector images, models, fiducials complete. Patterns for diffusion weighted completed, fMRI ongoing.<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Create frameworks<br />
| Yr 3<br />
| Frameworks for processing scalar and vector images, models, fiducials complete. Frameworks for diffusion weighted completed, fMRI ongoing.<br />
|-<br />
| '''GE'''<br />
| 2<br />
| '''Software engineering process'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Extreme programming<br />
| Yr 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Process automatiion<br />
| Yr 3<br />
| On schedule, ongoing<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Refactoring<br />
| Yr 3<br />
| Complete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| '''Automated quality system'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 3<br />
| DART deployment<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| Persistent testing system<br />
| Yr 5<br />
| Incomplete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| Automatic defect detection<br />
| Yr 5<br />
| Incomplete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| '''Cross-platform development'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Deploy environment (CMake, CTest)<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| DART Integration and testing<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Documentation tools<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| '''Integration tools'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| File Formats/IO facilities<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| CableSWIG deployment<br />
| Yr 3<br />
| Complete (integration ongoing)<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Establish XML schema<br />
| Yr 4<br />
| Complete, refinements ongoing<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| '''Technology delivery'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Deploy applications<br />
| Yr 1<br />
| Complete (ongoing)<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Establish plug-in repository<br />
| Yr 2<br />
| Incomplete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Cpack<br />
| Yr 4-5<br />
| Incomplete<br />
|-<br />
| '''Isomics'''<br />
| 1<br />
| NAMIC builds of slicer<br />
| Years 2--5<br />
| Complete<br />
|-<br />
| '''Isomics'''<br />
| 1<br />
| Schizophrenia and DBP intefaces<br />
| Year 3---5<br />
| Completed (refinements ongoing)<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| ITK Integration tools<br />
| Year 1---3<br />
| Completed<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| Experiment Control Interfaces<br />
| Year 2---5<br />
| Migration from LONI to BatchMake Underway<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| fMRI/DTI algorithm support<br />
| Year 2---5<br />
| Completed DTI, fMRI Ongoing<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| New DBP algorithm support<br />
| Year 2---5<br />
| Ongoing<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Compatible build process<br />
| Year 1---3<br />
| Completed<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Dart Integration<br />
| Year 1---2<br />
| Completed (upgrades ongoing)<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Test scripts for new code<br />
| Year 2---5<br />
| Ongoing<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Grid computing---base<br />
| Year 1<br />
| Completed<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Grid enabled algorithms<br />
| Year 3<br />
| First version (GWiz alpha) available - initial integration with Slicer3 and execution model.<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Testing infrastructure<br />
| Year 4<br />
| Initiated<br />
|-<br />
| '''UCSD'''<br />
| 2<br />
| Data grid --- compatibility<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''UCSD'''<br />
| 2<br />
| Data grid --- slicer access<br />
| Year 2<br />
| Completed for version 2.6. In progress for Slicer3<br />
|-<br />
| '''UCSD'''<br />
| 3<br />
| Data mediation --- deploy<br />
| Year 1<br />
| Incomplete (modfication below)<br />
|-<br />
| '''UCLA'''<br />
| 1<br />
| Debabeler functionality<br />
| Year 1<br />
| Continued Progress<br />
|-<br />
| '''UCLA'''<br />
| 2<br />
| SLIPIE Interpretation (Layer 1)<br />
| Year 1--Year2<br />
| In Progress<br />
|-<br />
| '''UCLA'''<br />
| 3<br />
| SLIPIE Interpretation (Layer 2)<br />
| Year 1--Year2<br />
| On Schedule<br />
|-<br />
| '''UCLA'''<br />
| 3<br />
| Developing ITK Modules<br />
| Year2<br />
| In Progress<br />
|-<br />
| '''UCLA'''<br />
| 4<br />
| Integrating SRB (GSI-enabled)<br />
| Year2<br />
| Completed<br />
|-<br />
| '''UCLA'''<br />
| 5<br />
| Integrating IDA<br />
| Year2<br />
| Completed<br />
|-<br />
| '''UCLA'''<br />
| 5<br />
| Integrating External Visualization Applications<br />
| Year2<br />
| Completed<br />
|}<br />
<br />
=== Core 2 Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Data mediation<br />
| Delayed pending integration of databases into NAMIC infractructure<br />
|}<br />
<br />
=== [[Core_2_Timeline_Notes|Core 2 Timeline Notes ]] ===<br />
<br />
== Core 3: Driving Biological Problems ==<br />
<br />
The Core 3 projects submitted R01 style proposals, as specified in the RFA, and did not submit timelines.<br />
<br />
== Core 4: Service ==<br />
<br />
=== Core 4 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| '''Implement Development Farms'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Deploy platforms<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Communications<br />
| Yrs 1<br />
| Complete, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| '''Establish software process'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Secure developer database<br />
| Yr 1<br />
| Complete, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Collect guidelines<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Manage software submission process<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Configure process tools<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Survey community<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| '''Deploy NAMIC Tools'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Toolkits<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Integration tools<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Applications<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Integrate new computing resources<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| '''Provide support'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| Esablish support infrastructure<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| NAMIC support<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 5<br />
| Manage NAMIC Software Releases<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|}<br />
<br />
=== Core 4 Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| Kitware<br />
| 2-5<br />
| Various<br />
| Refined/modified the sub aims<br />
|}<br />
<br />
=== [[Core_4_Timeline_Notes|Core 4 Timeline Notes ]] ===<br />
<br />
== Core 5: Training ==<br />
<br />
=== Core 5 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| '''Formal Training Guidllines'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| Functional neuroanatomy<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| Clinical correlations<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| '''Mentoring'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| Programming workshops<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| One-on-one mentoring, Cores 1, 2, 3<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| '''Collaborative work environment'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Wiki<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Mailing lists<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Regular telephone conferences<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 4<br />
| '''Educational component for tools'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 4<br />
| Slicer training modules<br />
| Yr 2-5<br />
| Slicer 2.x tutorials complete, Two Slicer 3 tutorials complete, translation of 2.x tutorials to 3 is ongoing and on schedule<br />
|-<br />
| '''Harvard'''<br />
| 5<br />
| '''Demonstrations and hands-on training'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 5<br />
| Various workshops and conferences<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|}<br />
<br />
=== Core 5 Timeline Modifications ===<br />
<br />
None.<br />
<br />
=== [[Core_5_Timeline_Notes|Core 5 Timeline Notes ]] ===<br />
<br />
== Core 6: Dissemination ==<br />
<br />
=== Core 6 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| Isomics<br />
| 1<br />
| Create a collaboration metholdology for NA-MIC<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 1.1<br />
| develop a selection process<br />
| Yr 1<br />
| Complete<br />
|-<br />
| Isomics<br />
| 1.2<br />
| guidelines to govern the collaborations<br />
| Yr 1-2<br />
| Complete<br />
|-<br />
| Isomics<br />
| 1.3<br />
| Provide on-site training<br />
| Yr 1-5<br />
| Complete for current tools (ongoing for tool refinement)<br />
|-<br />
| Isomics<br />
| 1.4<br />
| develop a web site infrastructure<br />
| Yr 1<br />
| Complete<br />
|-<br />
| Isomics<br />
| 2<br />
| Facilitate communication between NA-MIC developers and wider research community<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 2.1<br />
| develop materials describing NAMIC technology<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.2<br />
| participate in scientific meetings<br />
| Yr 2-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.3<br />
| Document interactions with external researchers<br />
| Yr 2-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.4<br />
| Coordinate publication strategies<br />
| Yr 3-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3<br />
| Develop a publicly accessible internet resource of data, software, documentation, and publication of new discoveries<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 3.1<br />
| On-line repository of NAMIC related publications and presentations<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3.2<br />
| On-line repository of NAMIC tutorial and training material<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3.3<br />
| Index and a searchable database<br />
| Yr 1-2<br />
| Done<br />
|-<br />
| Isomics<br />
| 3.4<br />
| Automated feedback systems that track software downloads<br />
| Yr 3<br />
| Done<br />
|}<br />
<br />
=== Core 6 Timeline Modifications ===<br />
<br />
None.<br />
<br />
=== [[Core_6_Timeline_Notes|Core 6 Timeline Notes ]] ===<br />
<br />
=Appendix A Publications (Mastrogiacomo)=<br />
A list should be mined from the publications database and attached here in MS word format.<br />
<br />
=Appendix B EAB Report and Response (Kapur)=<br />
===EAB Report===<br />
<br />
===Response to EAB Report===</div>Kubickihttps://www.na-mic.org/w/index.php?title=2009_Annual_Scientific_Report&diff=362902009 Annual Scientific Report2009-04-15T17:53:52Z<p>Kubicki: /* Algorithm Component (Golland) */</p>
<hr />
<div>Back to [[2009_Progress_Report]]<br />
<br />
<br />
<br />
<br />
<br />
=Guidelines for preparation=<br />
<br />
*[[2009_Progress_Report#Scientific Report Timeline]] - Main point is that May 15 is the date by which all sections below need to be completed. No extensions are possible.<br />
*DBPs - If there is work outside of the roadmap projects that you would like to report, you are welcome to create a separate section for it under "Other". <br />
*The outline for this report is similar to the 2008 and 2007 reports, which are provided here for reference: [[2008_Annual_Scientific_Report]], [[2007_Annual_Scientific_Report]].<br />
*In preparing summaries for each of the 8 topics in this report, please leverage the detailed pages for projects provided here: [[NA-MIC_Internal_Collaborations]].<br />
*Publications will be mined from the SPL publications database. All core PIs need to ensure that all NA-MIC publications are in the publications database by May 15.<br />
<br />
=Introduction (Tannenbaum)=<br />
<br />
The National Alliance for Medical Imaging Computing (NA-MIC) is now in its fifth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. This was our second year with our current DBPS of which three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. The and fourth is a very new direction, the prostate: brachytherapy needle positioning robot integration.<br />
<br />
We briefly summarize the work of NAMIC during the five years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main themes: Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. The third year of center activity was devoted to the continuation of the collaborative efforts in order to give solutions to the various brain-oriented DBPs. The fourth year was focused on translating our work to the new DBPs. In the fifth year, a number of projects reached the point where modules were introduced<br />
into Slicer making the Core 1 algorithms available to the general medical imaging community. A number of the algorithms are quite general and can be used for purposes much broader than the original DBPs. For example, a new point cloud registration algorithm was developed for the prostate brachytherapy needle positioning project can be used also for DWI registration.<br />
Work on DTI/DWI tractography has impacted the segmentation of blood vessels and soft plaque detection in the coronaries.<br />
<br />
Year five has seen progress with the work of our current DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.<br />
<br />
In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent work on the four infrastructure topics. These include: Diffusion Image analysis (Section 4.1), Structural analysis (Section 4.2), Functional MRI analysis (Section 4.3), and the NA-MIC Toolkit (Section 4.4). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final sections of this report, Sections 5-11, provide updated timelines on the status of the various projects of the different cores of NAMIC.<br />
<br />
=Clinical Roadmap Projects=<br />
==Roadmap Project: Stochastic Tractography for VCFS (Kubicki)==<br />
===Overview (Kubicki)===<br />
The goal of this project is to create an end-to-end application that would be useful in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-facial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.<br />
<br />
===Algorithm Component (Golland)===<br />
At the core of this project is the stochastic tractography algorithm<br />
developed and implemented in collaboration between MIT and<br />
BWH. Stochastic Tractography is a Bayesian approach to estimating<br />
nerve fiber tracts from DTI images.<br />
<br />
We first use the diffusion tensor at each voxel in the volume to<br />
construct a local probability distribution for the fiber direction<br />
around the principal direction of diffusion. We then sample the tracts<br />
between two user-selected ROIs, by simulating a random walk between<br />
the regions, based the local transition probabilities inferred from<br />
the DTI image.<br />
<br />
The resulting collection of fibers and the associated FA values<br />
provide useful statistics on the properties of connections between the<br />
two regions. To constrain the sampling process to the relevant white<br />
matter region, we use atlas-based segmentation to label ventricles and<br />
gray matter and to exclude them from the search space. As such, this<br />
step relies heavily on the registration and segmentation functionality<br />
in Slicer.<br />
<br />
Over the last year, we have been working on applying several pre- and postprocessing steps to the algorithm pipeline. These steps include eddy current and geometric distortion correction that have been made available to us by Utah group, as well as DTI filtering (BWH). White matter masks can also now be created based on T2 thresholding within the slicer stochastic tractography module, which makes them more precise, since they do not rely on MRI to DTI co-registration. <br />
<br />
At the same time we are working on the datasets where fMRI activations as well as gray matter segmentations need to be registered to DTI data, in order to seed within the predefined gray matter regions. We have made a significant progress in between modality registration, additional improvement is expected when distortion correction become part of the analysis pipeline. <br />
<br />
We are also working on improved ways to visualize and quantify stochastic tractography output, not only by parametrizing fiber tracts, but also by creating connection probability distribution maps.<br />
<br />
===Engineering Component (Davis)===<br />
<Note Progress in the last year><br />
<br />
===Clinical Component (Kubicki)===<br />
<Note Progress in the last year><br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard:Brain_Segmentation_Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==<br />
===Overview (Fichtinger)===<br />
Numerous studies have demonstrated the efficacy of image-guided<br />
needle-based therapy and biopsy in the management of prostate<br />
cancer. The accuracy of traditional prostate interventions performed using<br />
transrectal ultrasound (TRUS) is limited by image fidelity, needle<br />
template guides, needle deflection and tissue deformation. Magnetic Resonance<br />
Imaging (MRI) is an ideal modality for guiding and monitoring<br />
such interventions due to its excellent visualization of the prostate, its<br />
sub-structure and surrounding tissues. <br />
<br />
We have designed a comprehensive robotic assistant system that allows prostate biopsy and brachytherapy procedures to be performed entirely inside a 3T closed MRI scanner. The current system applies transrectal approach to the prostate: an endorectal coil and steerable needle guide, both tuned to 3T magnets and invariable to any particular scanner, are integrated into the MRI compatible manipulator.<br />
<br />
Under the NAMIC initiative, the image computing, visualization, intervention planning, and kinematic planning interface is being accomplished with open source system built on the NAMIC toolkit and its components, such as Slicer3 and ITK. These are complemented by a collection of unsupervised prostate segmentation and registration methods that are of great importance to the clinical performance of the interventional system as a whole.<br />
<br />
===Algorithm Component (Tannenbaum)===<br />
<br />
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps below.<br />
<br />
'''Prostate Segmentation'''<br />
<br />
We must first extract the prostate. We provided two methods: a shape based method and a semi-automatic method. More details are given below and images and further details may be found [http://www.na-mic.org/Wiki/index.php/Projects:ProstateSegmentation here]<br />
<br />
# ''A shape based algorithm''. This begins with learning a group of shapes, obtained from manually segmenting a set of prostate 3D images. With the shapes represented as the hyperbolic tangent of the signed distance functions, principle component analysis is employed to learn the shapes. Further, given a new prostate image, we search the learned shape space in order to find one shape best segment the given image. The fitness of one shape to segment the image is evaluated by an energy functional measuring the discrepancy of the statistical characteristics inside and outside the current segmentation boundary. Such method is robust to the noise in the images. Moreover, the whole algorithm pipeline has been integrated into the Slicer3 through the command line module.<br />
# ''Semi-automatic method''. This method is based on a random walk segmentation algorithm. With user provided initial seed regions inside and out side the object (prostate), the algorithm computes a probability distribution over the image domain by solving a boundary value partial differential equation where the value at seed regions are fixed at 1.0 or 0.0, depending or whether they are object or background seeds. The resulting distribution indicates the probability of each voxel belonging to the object. Simply threshold by 0.5 gives the segmentation of the object. Moreover, if the result is not suitable, the user can edit the seed regions, and the new result is computed based on this previous result. This algorithm has been integrated into the transrectal prostate MRI module of Slier3.<br />
<br />
'''Prostate Registration'''<br />
<br />
We developed a nonlinear (affine) prostate registration method by treating prostate images as point sets. Then the iterative closest point algorithm is improved to register the point sets generated by the two images to be registered. The proposed method shows robustness to long distance transition and partial image structure. Moreover, such representation is much sparser than sampling image on the uniform grid thus the registration is very fast comparing two 3D volumetric<br />
image registration.<br />
<br />
Furthermore, the registration is viewed as a posterior estimation problem, in which the distributions of the affine and translation parameters are to be estimated. This can naturally be estimated using a particle filter framework. Through this, the method can handle the otherwise difficult cases where the two prostates are one supine and<br />
one prone.<br />
<br />
More details are given [[Projects:pfPtSetImgReg|here...]]<br />
<br />
===Engineering Component (Hayes)===<br />
<Note Progress in the last year><br />
<br />
<br />
===Clinical Component (Fichtinger)===<br />
<Note Progress in the last year><br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:JHU:Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus (Bockholt)==<br />
===Overview (Bockholt)===<br />
The primary goal of the MIND DPB is to examine changes in white matter lesions in adults with Neuropsychiatric Systemic Lupus Erythematosus (SLE). We want to be able to characterize lesion location, size, and intensity, and would also like to examine longitudinal changes of lesions in an SLE cohort. To accomplish this goal, we will create an end-to-end application entirely within NA-MIC Kit allowing individual analysis of white matter lesions. Such a workflow will then be applied to a clinical sample in the process of being collected.<br />
<br />
===Algorithm Component (Whitaker)===<br />
The basic steps necessary for the white matter lesion analysis application entail first registration of T1, T2, and FLAIR images, second tissue classification into gray, white, csf, or lesion, thirdly clustering lesion for anatomical localization, and finally a summarization of lesion size and image intensity parameters within each unique lesion. <br />
<br />
<Note Progress in the last year><br />
<br />
===Engineering Component (Pieper)===<br />
<Note Progress in the last year><br />
<br />
===Clinical Component (Bockholt)===<br />
<Note Progress in the last year><br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND:Roadmap here on the NA-MIC wiki].<br />
==Roadmap Project: Cortical Thickness for Autism(Hazlett)== <br />
===Overview (Hazlett)===<br />
<br />
A primary goal of the UNC DPB is to examine changes in cortical thicknes in children with autism compared to typical controls. We want to examine group differences in both local and regional cortical thickness, and would also like to examine longitudinal changes in the cortex from ages 2-4 years. To accomplish this goal, this project will create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness. Such a workflow will then be applied to our study data (already collected).<br />
<br />
===Algorithm Component (Styner)===<br />
<br />
The basic steps necessary for the cortical thickness application entail first tissue segmentation in order to separate white and gray matter regions, second cortical thickness measurement, thirdly cortical correspondence to compare measurements across subjects and finally a statistical analysis to locally compute group differences.<br />
<br />
<Note Progress in the last year><br />
<br />
===Engineering Component (Miller, Vachet)===<br />
<br />
<Note Progress in the last year><br />
<br />
===Clinical Component (Hazlett)===<br />
<Note Progress in the last year><br />
<br />
===Additional Information===<br />
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNC:Cortical_Thickness_Roadmap here on the NA-MIC wiki].<br />
<br />
=Four Infrastructure Topics=<br />
==Diffusion Image Analysis (Gerig)==<br />
<Note Progress in the last year><br />
===Key Investigators===<br />
<br />
<Need to update the list below><br />
<br />
* BWH: Marek Kubicki, Martha Shenton, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Carl-Fredrik Westin, Gordon Kindlmann<br />
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo<br />
* UCI: James Fallon<br />
* Utah I: Tom Fletcher, Ross Whitaker, Ran Tao, Yongsheng Pan<br />
* Utah II: Casey Goodlett, Sylvain Gouttard, Guido Gerig<br />
* GA Tech: John Melonakos, Vandana Mohan, Shawn Lankton, Allen Tannenbaum<br />
* GE: Xiaodong Tao, Jim Miller<br />
* Isomics: Steve Pieper<br />
* Kitware: Luis Ibanez<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:DiffusionImageAnalysis here on the NA-MIC wiki].<br />
<br />
==Structural Analysis(Tannenbaum)==<br />
===Progress===<br />
Under Structural Analysis, the main topics of research for NAMIC are structural segmentation, registration techniques and shape analysis. These topics are correlated and hence research in one often finds application in another. For example, shape analysis can yield useful priors for segmentation, or segmentation and registration can provide structural correspondences for use in shape analysis and so on. <br />
<br />
An overview of selected progress highlights under these broad topics follows:<br />
<br />
Segmentation<br />
<br />
* Geodesic Tractography Segmentation: We proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. This has been applied successfully to the segmentation of neural fiber bundles such as the Cingulum Bundle. This framework has now been integrated into Slicer and is being tested on a population of brain data sets.<br />
<br />
* Tubular Surface Segmentation: We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels. <br />
<br />
* Local-global Segmentation: We have proposed a novel segmentation approach that combines the advantages of local and global approaches to segmentation, by using statistics over regions that are local to each point on the evolving countour. This makes it well suited to applications with contrast differences within the structure of interest such as in blood vessel segmentation, as well as applications like the neural fiber bundles where the diffusion profiles of voxels within the structure are locally similar but vary along the length of the fiber bundle itself.<br />
<br />
* Shape-based segmentation: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have presented an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. We have applied the algorithm successfully to real MRI images, and we have also implemented it into 3D Slicer.<br />
<br />
* Re-Orientation Approach for Segmentation of DW-MRI: This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation which allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares very favorably with segmentation by full-brain streamline tractography. <br />
<br />
<br />
Registration<br />
<br />
* Optimal Mass Transport based Registration: We have provided a computationaly efficient non-rigid/elastic image registration algorithm based on the Optimal Mass Transport theory. We use the Monge-Kantorovich formulation of the Optimal Mass Transport problem and implement the solution proposed by Haker et al. using multi-resolution and multigrid techniques to speed up the convergence. We also leverage the computation power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We extend the work by Haker et al. who compute the optimal warp from a first order partial differential equation, an improvement over earlier proposed higher order methods and those based on linear programming, and further implement the algorithm using a coarse-to-fine strategy resulting in phenomenol improvement in convergence. We have applied it successfully to the registration of 3D brain MRI datasets (preoperative and intra-operative), and are currently extending it to the non-rigid registration of baseline DWI to brain MRI data.<br />
<br />
* Atlas Regularization for Image Segmentation: Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. <br />
<br />
* Point-set Rigid Registration: We have proposed a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation. Moreover, we incorporate stochastic dynamics to model the uncertainity of the registration process. We treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temoral coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets.We applied the algorithm to different alignments of point clouds and it successfully found the correct optimal transformation that aligns two given point clouds despite the differing geometry around the local neighborhood of a point within their respective sets. <br />
<br />
* Regularization for Optimal Mass Transport: To extend the flexibility of the existing OMT algorithm, we added a regularization term to the functional being minimized. This term controls the tradeoff between how well two images match after registration versus how warped the transformation map can become. A weighted sum of squared differences is used to penalize having to move mass over long distances; this addition also helps to keep the transformation physically accurate by reducing the likelihood that the transformation grid will fold over itself and keeping the grid smooth.<br />
<br />
* Registration of DW-MRI to structural MRI: Optimal Mass Transport was applied to the problem of correcting EPI distortion in DW-MRI. A mask for white matter in DW-MRI was registered to the white matter mask extracted from the structural MRI for the same patient. Prior to registration, it is important to normalize intensities in the two masks; this was done by dividing the images into regions and uniformly normalizing over each region to assure the sum of the intensities is equal. Then, once a transformation between the white matter masks was calculated, this transformation was applied to the original DW-MRI image. <br />
<br />
Shape Analysis<br />
<br />
* Shape Analysis Framework using SPHARM-PDM: We have provided an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are first processed to fill any interior holes. The processed binary segmentations are converted to surface meshes, and a spherical parametrization is computed for the surface meshes using a area-preserving, distortion minimizing spherical mapping. The SPHARM description is computed from the mesh and its spherical parametrization. Using the first order ellipsoid from the spherical harmonic coefficients, the spherical parametrizations are aligned to establish correspondence across all surfaces. The SPHARM description is then sampled into a triangulated surfaces (SPHARM-PDM) via icosahedron subdivision of the spherical parametrization. These SPHARM-PDM surfaces are all spatially aligned using rigid Procrustes alignment. Group differences between groups of surfaces are computed using the standard robust Hotelling T 2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. We provide additional visualization of the group tests via mean difference magnitude and vector maps, as well as maps of the group covariance information. We have a stable implementation, and current development focuses on integrating the current command line tools into Slicer (v3) via the Slicer execution model. <br />
<br />
* Population studies using Tubular Surface Model: We have proposed a tubular shape model for the Cingulum Bundle which models a tubular surface as a center-line coupled with a radius function at every point along the center-line. This model shows potential for population studies on the Cingulum Bundle which is believed to be involved in Schizophrenia, since it provides a natural way of sampling the structure to build a feature representation of it. We are currently segmenting the Cingulum Bundle from a population of brain data sets, towards performing this population analysis using the Pott's Model.<br />
<br />
* Automatic Outlining of Sulci on a Brain Surface: We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain, where the data is taken to be a 3D triangulated mesh formed from the segmentation of MR image slices. The problem is posed as energy minimization using penalizing the arc-length of segmenting curve using conformal factor involving the mean curvature of the underlying surface. The computation is made practical for dense meshes via the use of a sparse-field method to track the level set interfaces and regularized least-squares estimation of geometric quantities.<br />
<br />
===Key Investigators===<br />
<br />
Needs to be updated:<br />
<br />
* MIT: Polina Golland, Kilian Pohl, Sandy Wells, Eric Grimson, Mert R. Sabuncu<br />
* UNC: Martin Styner, Ipek Oguz, Xavier Barbero <br />
* Utah: Ross Whitaker, Guido Gerig, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer <br />
* GaTech: Allen Tannenbaum, John Melonakos, Vandana Mohan, Tauseef ur Rehman, Shawn Lankton, Samuel Dambreville, Yi Gao, Romeil Sandhu, Xavier Le Faucheur, James Malcolm, Ivan Kolosev<br />
* Isomics: Steve Pieper <br />
* GE: Bill Lorensen, Jim Miller <br />
* Kitware: Luis Ibanez, Karthik Krishnan<br />
* UCLA: Arthur Toga, Michael J. Pan, Jagadeeswaran Rajendiran <br />
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt, Yogesh Rathi, Marek Kubicki, Steven Haker<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:StructuralImageAnalysis here on the NA-MIC wiki].<br />
==fMRI Analysis (Golland)==<br />
===Progress===<br />
One of the major goals in analysis of fMRI data is the detection of<br />
functionally homogeneous networks in the brain. <br />
<br />
<note progress here><br />
<br />
===Key Investigators===<br />
<br />
Need to update this list:<br />
<br />
# MIT: Polina Golland, Danial Lashkari, Bryce Kim <br />
# Harvard/BWH: Sylvain Bouix, Martha Shenton, Marek Kubicki<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:fMRIAnalysis here on the NA-MIC wiki].<br />
==NA-MIC Kit Theme (Schroeder)==<br />
===Progress===<br />
The NAMIC-Kit consists of a framework of advanced computational components, as well as the support infrastructure for testing, documenting, and deploying leading edge medical imaging algorithms and software tools. The framework has been carefully constructed to provide low-level access to libraries and modules for advanced users, plus high-level application access that non-computer professionals can use to address a variety of problems in biomedical computing. In this fifth year of the NA-MIC projects <summary of progress><br />
<br />
===Software Releases===<br />
The NAMIC-Kit can be represented as a pyramid of capabilities, with the base consisting of toolkits and libraries, and the apex standing in for the Slicer3 user application. In between, Slicer modules are stand-alone executables that can be integrated directly into the Slicer3 application, including GUI integration, while work-flows are groups of modules that are integrated together to manifest sophisticated segmentation, registration and biomedical computing algorithms. In a coordinated NAMIC effort, major releases of these many components were realized over the past year. This includes, but is not limited to:<br />
*<br />
*<br />
<br />
===Slicer3 and the Software Framework===<br />
One of the major achievements of the past year has been...<br />
<br />
===Software Process===<br />
One of the challenges facing developers has been the requirement to implement, test and deploy software systems across multiple computing platforms. NAMIC continues to push the state of the art with further development of the CMake, CTest, and CPack tools for cross-platform development, testing, and packaging, respectively...<br />
<br />
===Key Investigators===<br />
THis list needs to be updated:<br />
<br />
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman<br />
* GE - Jim Miller, Xiaodong Tao<br />
* Isomics - Steve Pieper<br />
<br />
===Additional Information===<br />
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC-Kit here on the NA-MIC wiki].<br />
<br />
<br />
<br />
=Highlights(Schroeder)=<br />
===Advanced Algorithms===<br />
<br />
===NAMIC-Kit===<br />
<br />
===Outreach and Technology Transfer===<br />
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.<br />
* The Slicer community held several workshops and tutorials. In xxx a satellite event was held for the international Organization for Human Brain Mapping at the annual meeting in xxx. The xx workshop on xx hosted xx participants representing xx countries from around the world, xx states within the US and xxdifferent laboratories including xx NIH institutes. In addition, <note how many slicer tutorials were held and where etc><br />
* Project Week continues to be a successful NAMIC venue. These semi-annual events are held in Boston in June, and January in Salt Lake City. These events are well attended with approximately 100 participants, of which about a third are outside collaborators. At the last Project Week in Salt Lake City, approximately xx projects were realized.<br />
* NAMIC continues to participate in conferences and other technical venues. For example, NAMIC hosted xxx<br />
<br />
<br />
=Impact and Value to Biocomputing (Miller)=<br />
NA-MIC impacts Biocomputing through a variety of mechanisms. First,<br />
NA-MIC produces scientific results, methodologies, workflows,<br />
algorithms, imaging platforms, and software engineering tools and<br />
paradigms in an open enviroment that contributes directly to the body of<br />
knowledge available to the field. Second, NA-MIC science and<br />
technology enables the entire medical imaging community to build on<br />
NA-MIC results, methods, and techniques, to concentrate on the new<br />
science instead of developing supporting infrastructure, to leverage<br />
NA-MIC scientists and engineers to adapt NA-MIC technology to new<br />
problem domains, and to leverage NA-MIC infrastructure to distribute<br />
their own technology to a larger community.<br />
<br />
===Impact within the Center===<br />
<br />
===Impact within NIH Funded Research===<br />
<br />
===National and International Impact===<br />
<br />
<br />
= Timeline (Ross)=<br />
<br />
<The table needs to be updated><br />
<br />
<br />
This section of the report gives the milestones for years 1 through 5 that are associated with the timelines in the original proposal. We have organized the milestones by core. For each milestone we have indicated the proposed year of completion and a very brief description of the current status. In some cases the milestones include ongoing work, and we have try to indicate that in the status. We have also included tables that list any significant changes to the proposed timelines. On the wiki page, we have links to the notes from the various PIs that give more details on their progress and the status of the milestones.<br />
<br />
'''These tables demonstrate that the project is, on the whole, proceeding according to the originally planned schedule.'''<br />
<br />
<br />
<br />
== Core 1: Algorithms ==<br />
<br />
=== Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''MIT'''<br />
| 1<br />
| '''Shape-based segmentation'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 1.1<br />
| Methods to learn shape representations<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 1.2<br />
| Shape in atlas-driven segmentation<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 1.3<br />
| Validate and refine approach<br />
| Year 5<br />
| In Progress<br />
|-<br />
| '''MIT'''<br />
| 2<br />
| '''Shape analysis'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 2.1<br />
| Methods to compute statistics of shapes<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 2.3<br />
| Validation of shape methods on application data<br />
| Year 5<br />
| Completed, refinements ongoing<br />
|-<br />
| '''MIT'''<br />
| 3<br />
| '''Analysis of DTI data'''<br />
|<br />
|<br />
|-<br />
| '''MIT'''<br />
| 3.1<br />
| Fiber geometry<br />
| Year 3<br />
| Completed<br />
|-<br />
| '''MIT'''<br />
| 3.2<br />
| Fiber statistics<br />
| Year 5<br />
| Completed, new developments ongoing<br />
|-<br />
| '''MIT'''<br />
| 3.3<br />
| Validation on real data<br />
| Year 5<br />
| Completed, refinements ongoing<br />
|-<br />
| '''Utah'''<br />
| 1<br />
| '''Processing of DTI data'''<br />
|<br />
|<br />
|-<br />
| '''Utah'''<br />
| 1.1<br />
| Filtering of DTI<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''Utah'''<br />
| 1.2<br />
| Quantitative analysis of DTI<br />
| Year 3<br />
| Completed, refinements ongoing<br />
|-<br />
| '''Utah'''<br />
| 1.3<br />
| Segmentation of cortex/WM<br />
| Year 3<br />
| Completed partially, modified below<br />
|-<br />
| '''Utah'''<br />
| 1.4<br />
| Segmentation analysis of white matter tracts<br />
| Year 3<br />
| Completed, applications ongoing<br />
|-<br />
| '''Utah'''<br />
| 1.5<br />
| Joint analysis of DTI and functional data<br />
| Year 5<br />
| Initiated<br />
|-<br />
| '''Utah'''<br />
| 2<br />
| Nonparametric Shape Analysis<br />
| Year 5<br />
| Completed<br />
|-<br />
| '''Utah'''<br />
| 2.1<br />
| Framework in place<br />
| Year 3<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.2<br />
| Demonstration on shape of neuranatomy (from Core 3)<br />
| Year 4<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.3<br />
| Development for multiobject complexes<br />
| Year 4<br />
| Complete<br />
|-<br />
| '''Utah'''<br />
| 2.4<br />
| Demonstration of NP shape representations on clinical hypotheses from Core 3<br />
| Year 5<br />
| Complete, publications in progress<br />
|-<br />
| '''Utah'''<br />
| 2.6<br />
| Integration into NAMIC-kit<br />
| Year 5<br />
| Incomplete (initiated)<br />
|-<br />
| '''Utah'''<br />
| 2.7<br />
| Shape regression<br />
| Year 5<br />
| Incomplete<br />
|-<br />
<br />
|-<br />
| '''UNC'''<br />
| 1<br />
| '''Statistical shape analysis'''<br />
|<br />
|<br />
|-<br />
| '''UNC'''<br />
| 1.1<br />
| Comparative anal. of shape anal. schemes<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 1.3<br />
| Statistical shape analysis incl. patient variable<br />
| Year 5<br />
| Complete, refinements ongoing<br />
|-<br />
| '''UNC'''<br />
| 2<br />
| '''Structural analysis of DW-MRI'''<br />
|<br />
|<br />
|-<br />
| '''UNC'''<br />
| 2.1<br />
| DTI tractography tools<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 2.2<br />
| Geometric characterization of fiber tracts<br />
| Year 5<br />
| Completed<br />
|-<br />
| '''UNC'''<br />
| 2.3<br />
| Quant. anal. of diffusion along fiber tracts<br />
| Year 5<br />
| Completed.<br />
|-<br />
| '''GaTech'''<br />
| 1.1<br />
| ITK Implementation of PDEs<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.1<br />
| Applications to Core 3 data<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.2<br />
| New statistic models<br />
| Year 4<br />
| Completed<br />
|-<br />
| '''GaTech'''<br />
| 1.2<br />
| Shape anaylsis<br />
| Year 4<br />
| Completed, refinements ongoing<br />
|-<br />
| '''GaTech'''<br />
| 2.0<br />
| Integration in to Slicer<br />
| Year 4-5<br />
| Preliminary results and ongoing<br />
|-<br />
| '''MGH'''<br />
| 1<br />
| '''Registration'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 1.1<br />
| Collect DTI/QBALL data<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 1.2<br />
| Develop registration method<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 1.3<br />
| Test/optimize registration method<br />
| Year 3<br />
| In Progress<br />
|-<br />
| '''MGH'''<br />
| 1.4<br />
| Apply registration on core 3 data<br />
| Year 5<br />
| In Queue<br />
|-<br />
| '''MGH'''<br />
| 2<br />
| '''Group DTI Statistics'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 2.1<br />
| Develop group statistic method<br />
| Year 2<br />
| Partially Complete<br />
|-<br />
| '''MGH'''<br />
| 2.2<br />
| Apply on core 3 data<br />
| Year 5<br />
| In Queue<br />
|-<br />
| '''MGH'''<br />
| 3<br />
| '''Diffusion Segmentation '''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 3.1<br />
| Collect DTI/QBALL data<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''MGH'''<br />
| 3.2<br />
| Develop/optimize segmentation algorithm<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 3.3<br />
| Integrate w/ tractography<br />
| Year 4<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 3.4<br />
| Apply on core 3 data<br />
| Year 5<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4<br />
| '''Group Morphometry Statistics'''<br />
|<br />
|<br />
|-<br />
| '''MGH'''<br />
| 4.1<br />
| Develop/optimize statistics algorithms<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.2<br />
| Develop GUI for Linux<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.3<br />
| Slicer integration<br />
| Year 3<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 4.4<br />
| Compile application on Windows<br />
| Year 4<br />
| Modified<br />
|-<br />
| '''MGH'''<br />
| 5<br />
| XNAT Desktop<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 5.1<br />
| Establish requirements for desktop version of XNAT <br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 5.2<br />
| Develop implementation plan for prototype<br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 5.3<br />
| Implement prototype version <br />
| Years 4-5<br />
| Incomplete (in progress)<br />
|-<br />
| '''MGH'''<br />
| 5.4<br />
| Implement alpha version<br />
| Year 5<br />
| Incomplete<br />
|-<br />
| '''MGH'''<br />
| 6<br />
| XNAT Central<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 6.1<br />
| Deploy XNAT Central, a public access XNAT host <br />
| Years 4-5<br />
| Complete<br />
|-<br />
| '''MGH'''<br />
| 6.2<br />
| Coordinate with NAMIC sites to upload project data<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 6.3<br />
| Continue developing XNAT Central based on feedback from NAMIC sites<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 7<br />
| NAMIC Kit integration<br />
| Years 4-5<br />
| <br />
|-<br />
| '''MGH'''<br />
| 7.1<br />
| Implement web services to exchange data with Slicer, Batchmake, and other client applications<br />
| Years 4-5<br />
| Incomplete (ongoing)<br />
|-<br />
| '''MGH'''<br />
| 7.2<br />
| Add XNAT Desktop to standard NAMIC kit distribution<br />
| Year 5<br />
| Incomplete<br />
|-<br />
|}<br />
<br />
=== Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| '''MIT'''<br />
| 2.2<br />
| Methods to compare shape statistics<br />
| Removed, the effort refocused on registration necessary for population studies <br />
|-<br />
| '''MIT'''<br />
| 2.4<br />
| Software infrastructure to integrate shape analysis tools into the pipeline for population studies.<br />
| New, morphed into collaboration with XNAT to provide more general population analysis tools. Partially completed.<br />
|-<br />
| '''MIT'''<br />
| 4<br />
| fMRI analysis including local and atlas-based priors for quantifying activation.<br />
| New, partially completed. Refinements in progress. Clinical study with Core 1 is in progress.<br />
|-<br />
| '''Utah'''<br />
| 2.2 (removed)<br />
| Feature-based brain image registration.<br />
| Shift emphasis to shape-based analysis/registration<br />
|-<br />
| '''Utah'''<br />
| 2.1 (removed)<br />
| Cortical filtering and feature detection<br />
| Effort is subsumed by other Core 1 partners (e.g. see MGH/Freesurfer)<br />
|-<br />
| '''Utah'''<br />
| 1.3 (removed)<br />
| Segmentation of cortex/WM<br />
| Effort is subsumed by other Core 1-2 partners (e.g. see EM-Segmenter)<br />
|-<br />
| '''Utah'''<br />
| 3.0 (removed)<br />
| Fast implmentations of PDEs<br />
| Real-time filtering is demphasized in favor of shape/DTI analysis<br />
|-<br />
| '''Utah'''<br />
| 1.5 (added)<br />
| Joint analysis of DTI and functional data<br />
| Opportunities/needs within various collaborations<br />
|-<br />
| '''Utah'''<br />
| 2.1-2.3 (added, in place of cortical analysis)<br />
| Shape analysis<br />
| Nonparametric shape analysis added to address needs of core 3.<br />
|-<br />
| '''Utah'''<br />
| 2.7<br />
| Shape regression<br />
| Extension/completion of framework. Opportunities/needs within various collaborations.<br />
|-<br />
| '''UNC'''<br />
| 1.2<br />
| Develop medially-based shape representation<br />
| Remove<br />
|-<br />
| '''UNC'''<br />
| 1.4<br />
| Develop generic cortical correspondence framework (Years 3-5)<br />
| New<br />
|-<br />
| '''UNC'''<br />
| 2.4<br />
| DTI Atlas Building (Years 2--4)<br />
| New<br />
|-<br />
| '''GaTech'''<br />
| 2.1<br />
| FA analysis<br />
| New<br />
|-<br />
| '''MGH'''<br />
| 4.1 - 4.4 <br />
| Group Morphometry Statistics<br />
| Added and then removed, based on personnel changes<br />
|-<br />
| '''MGH'''<br />
| 5-7 <br />
| XNAT<br />
| Added to support remote image database capabilities<br />
|}<br />
<br />
=== [[Core_1_Timeline_Notes|Core 1 Timeline Notes ]] ===<br />
<br />
== Core 2: Engineering ==<br />
<br />
=== Core 2 Timelines and Milestones ===<br />
<br />
<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''GE'''<br />
| 1<br />
| '''Define software architecture'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Object design<br />
| Yr 1<br />
| Completed<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Identify patterns<br />
| Yr 3<br />
| Patterns for processing scalar and vector images, models, fiducials complete. Patterns for diffusion weighted completed, fMRI ongoing.<br />
|-<br />
| '''GE'''<br />
| 1<br />
| Create frameworks<br />
| Yr 3<br />
| Frameworks for processing scalar and vector images, models, fiducials complete. Frameworks for diffusion weighted completed, fMRI ongoing.<br />
|-<br />
| '''GE'''<br />
| 2<br />
| '''Software engineering process'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Extreme programming<br />
| Yr 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Process automatiion<br />
| Yr 3<br />
| On schedule, ongoing<br />
|-<br />
| '''GE'''<br />
| 2<br />
| Refactoring<br />
| Yr 3<br />
| Complete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| '''Automated quality system'''<br />
|<br />
|<br />
|-<br />
| '''GE'''<br />
| 3<br />
| DART deployment<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| Persistent testing system<br />
| Yr 5<br />
| Incomplete<br />
|-<br />
| '''GE'''<br />
| 3<br />
| Automatic defect detection<br />
| Yr 5<br />
| Incomplete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| '''Cross-platform development'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Deploy environment (CMake, CTest)<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| DART Integration and testing<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Documentation tools<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| '''Integration tools'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| File Formats/IO facilities<br />
| Yr 2<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| CableSWIG deployment<br />
| Yr 3<br />
| Complete (integration ongoing)<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Establish XML schema<br />
| Yr 4<br />
| Complete, refinements ongoing<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| '''Technology delivery'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Deploy applications<br />
| Yr 1<br />
| Complete (ongoing)<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Establish plug-in repository<br />
| Yr 2<br />
| Incomplete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Cpack<br />
| Yr 4-5<br />
| Incomplete<br />
|-<br />
| '''Isomics'''<br />
| 1<br />
| NAMIC builds of slicer<br />
| Years 2--5<br />
| Complete<br />
|-<br />
| '''Isomics'''<br />
| 1<br />
| Schizophrenia and DBP intefaces<br />
| Year 3---5<br />
| Completed (refinements ongoing)<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| ITK Integration tools<br />
| Year 1---3<br />
| Completed<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| Experiment Control Interfaces<br />
| Year 2---5<br />
| Migration from LONI to BatchMake Underway<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| fMRI/DTI algorithm support<br />
| Year 2---5<br />
| Completed DTI, fMRI Ongoing<br />
|-<br />
| '''Isomics'''<br />
| 2<br />
| New DBP algorithm support<br />
| Year 2---5<br />
| Ongoing<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Compatible build process<br />
| Year 1---3<br />
| Completed<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Dart Integration<br />
| Year 1---2<br />
| Completed (upgrades ongoing)<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Test scripts for new code<br />
| Year 2---5<br />
| Ongoing<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Grid computing---base<br />
| Year 1<br />
| Completed<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Grid enabled algorithms<br />
| Year 3<br />
| First version (GWiz alpha) available - initial integration with Slicer3 and execution model.<br />
|-<br />
| '''UCSD'''<br />
| 1<br />
| Testing infrastructure<br />
| Year 4<br />
| Initiated<br />
|-<br />
| '''UCSD'''<br />
| 2<br />
| Data grid --- compatibility<br />
| Year 2<br />
| Completed<br />
|-<br />
| '''UCSD'''<br />
| 2<br />
| Data grid --- slicer access<br />
| Year 2<br />
| Completed for version 2.6. In progress for Slicer3<br />
|-<br />
| '''UCSD'''<br />
| 3<br />
| Data mediation --- deploy<br />
| Year 1<br />
| Incomplete (modfication below)<br />
|-<br />
| '''UCLA'''<br />
| 1<br />
| Debabeler functionality<br />
| Year 1<br />
| Continued Progress<br />
|-<br />
| '''UCLA'''<br />
| 2<br />
| SLIPIE Interpretation (Layer 1)<br />
| Year 1--Year2<br />
| In Progress<br />
|-<br />
| '''UCLA'''<br />
| 3<br />
| SLIPIE Interpretation (Layer 2)<br />
| Year 1--Year2<br />
| On Schedule<br />
|-<br />
| '''UCLA'''<br />
| 3<br />
| Developing ITK Modules<br />
| Year2<br />
| In Progress<br />
|-<br />
| '''UCLA'''<br />
| 4<br />
| Integrating SRB (GSI-enabled)<br />
| Year2<br />
| Completed<br />
|-<br />
| '''UCLA'''<br />
| 5<br />
| Integrating IDA<br />
| Year2<br />
| Completed<br />
|-<br />
| '''UCLA'''<br />
| 5<br />
| Integrating External Visualization Applications<br />
| Year2<br />
| Completed<br />
|}<br />
<br />
=== Core 2 Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| '''Isomics'''<br />
| 3<br />
| Data mediation<br />
| Delayed pending integration of databases into NAMIC infractructure<br />
|}<br />
<br />
=== [[Core_2_Timeline_Notes|Core 2 Timeline Notes ]] ===<br />
<br />
== Core 3: Driving Biological Problems ==<br />
<br />
The Core 3 projects submitted R01 style proposals, as specified in the RFA, and did not submit timelines.<br />
<br />
== Core 4: Service ==<br />
<br />
=== Core 4 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| '''Implement Development Farms'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Deploy platforms<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 1<br />
| Communications<br />
| Yrs 1<br />
| Complete, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| '''Establish software process'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Secure developer database<br />
| Yr 1<br />
| Complete, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Collect guidelines<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Manage software submission process<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Configure process tools<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 2<br />
| Survey community<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| '''Deploy NAMIC Tools'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Toolkits<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Integration tools<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Applications<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 3<br />
| Integrate new computing resources<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| '''Provide support'''<br />
|<br />
|<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| Esablish support infrastructure<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|-<br />
| '''Kitware'''<br />
| 4<br />
| NAMIC support<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Kitware'''<br />
| 5<br />
| Manage NAMIC Software Releases<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|}<br />
<br />
=== Core 4 Timeline Modifications ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Modification'''<br />
|-<br />
| Kitware<br />
| 2-5<br />
| Various<br />
| Refined/modified the sub aims<br />
|}<br />
<br />
=== [[Core_4_Timeline_Notes|Core 4 Timeline Notes ]] ===<br />
<br />
== Core 5: Training ==<br />
<br />
=== Core 5 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| '''Formal Training Guidllines'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| Functional neuroanatomy<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 1<br />
| Clinical correlations<br />
| Yr 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| '''Mentoring'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| Programming workshops<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 2<br />
| One-on-one mentoring, Cores 1, 2, 3<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| '''Collaborative work environment'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Wiki<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Mailing lists<br />
| Yrs 1<br />
| Complete<br />
|-<br />
| '''Harvard'''<br />
| 3<br />
| Regular telephone conferences<br />
| Yrs 1-5<br />
| On schedule, ongoing<br />
|-<br />
| '''Harvard'''<br />
| 4<br />
| '''Educational component for tools'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 4<br />
| Slicer training modules<br />
| Yr 2-5<br />
| Slicer 2.x tutorials complete, Two Slicer 3 tutorials complete, translation of 2.x tutorials to 3 is ongoing and on schedule<br />
|-<br />
| '''Harvard'''<br />
| 5<br />
| '''Demonstrations and hands-on training'''<br />
|<br />
|<br />
|-<br />
| '''Harvard'''<br />
| 5<br />
| Various workshops and conferences<br />
| Yrs 1--5<br />
| On schedule, ongoing<br />
|}<br />
<br />
=== Core 5 Timeline Modifications ===<br />
<br />
None.<br />
<br />
=== [[Core_5_Timeline_Notes|Core 5 Timeline Notes ]] ===<br />
<br />
== Core 6: Dissemination ==<br />
<br />
=== Core 6 Timelines and Milestones ===<br />
<br />
{| border="1"<br />
| '''Group'''<br />
| '''Aim'''<br />
| '''Milestone'''<br />
| '''Proposed time of completion'''<br />
| '''Status'''<br />
|-<br />
| Isomics<br />
| 1<br />
| Create a collaboration metholdology for NA-MIC<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 1.1<br />
| develop a selection process<br />
| Yr 1<br />
| Complete<br />
|-<br />
| Isomics<br />
| 1.2<br />
| guidelines to govern the collaborations<br />
| Yr 1-2<br />
| Complete<br />
|-<br />
| Isomics<br />
| 1.3<br />
| Provide on-site training<br />
| Yr 1-5<br />
| Complete for current tools (ongoing for tool refinement)<br />
|-<br />
| Isomics<br />
| 1.4<br />
| develop a web site infrastructure<br />
| Yr 1<br />
| Complete<br />
|-<br />
| Isomics<br />
| 2<br />
| Facilitate communication between NA-MIC developers and wider research community<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 2.1<br />
| develop materials describing NAMIC technology<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.2<br />
| participate in scientific meetings<br />
| Yr 2-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.3<br />
| Document interactions with external researchers<br />
| Yr 2-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 2.4<br />
| Coordinate publication strategies<br />
| Yr 3-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3<br />
| Develop a publicly accessible internet resource of data, software, documentation, and publication of new discoveries<br />
|<br />
|<br />
|-<br />
| Isomics<br />
| 3.1<br />
| On-line repository of NAMIC related publications and presentations<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3.2<br />
| On-line repository of NAMIC tutorial and training material<br />
| Yr 1-5<br />
| On Schedule<br />
|-<br />
| Isomics<br />
| 3.3<br />
| Index and a searchable database<br />
| Yr 1-2<br />
| Done<br />
|-<br />
| Isomics<br />
| 3.4<br />
| Automated feedback systems that track software downloads<br />
| Yr 3<br />
| Done<br />
|}<br />
<br />
=== Core 6 Timeline Modifications ===<br />
<br />
None.<br />
<br />
=== [[Core_6_Timeline_Notes|Core 6 Timeline Notes ]] ===<br />
<br />
=Appendix A Publications (Mastrogiacomo)=<br />
A list should be mined from the publications database and attached here in MS word format.<br />
<br />
=Appendix B EAB Report and Response (Kapur)=<br />
===EAB Report===<br />
<br />
===Response to EAB Report===</div>Kubickihttps://www.na-mic.org/w/index.php?title=AHM2009:PNL&diff=34620AHM2009:PNL2009-01-08T15:31:45Z<p>Kubicki: /* Team */</p>
<hr />
<div>[[AHM_2009#Agenda|Back to AHM 2009 Agenda]]<br />
<br />
__NOTOC__<br />
==PNL Roadmap Project==<br />
<br />
{|<br />
|[[Image:Helix.png|thumb|280x150px|Stochastic Tractography on helix phantom]]<br />
|[[Image:Wmm.png|thumb|280x150px|White Matter Mask generated from phantom]]<br />
|[[Image:STArcuate.jpg|thumb|280x150px|Stochastic Tractography of Arcuate Fasciculus]]<br />
|[[Image:Step1.png|thumb|380x250px|Stochastic Tractography Module]]<br />
|[[Image:Step2.png|thumb|180x120px|Stochastic Tractography Module]]<br />
<br />
|}<br />
<br />
== Overview ==<br />
;* What problem does the pipeline solve?<br />
Most tractography methods (Deterministic/Principal Diffusion Direction) estimate fibers by tracing the maximum direction of diffusion. <br />
<br />
A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. <br />
<br />
Stochastic tractography attempts to quantify the uncertainty associated with estimated fibers, by using a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths.<br />
<br />
Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. <br />
;* Who is the targeted user?<br />
Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. <br />
Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). <br />
;* How does the pipeline compare to state of the art?<br />
To date, there are two popular toolboxes that include probabilistic/stochastic tractography modules. Functionality of both modules (FSL and PICO) is limited, and software is not open source.<br />
<br />
==Detailed Information about the Pipeline==<br />
Stochastic Tractography pipeline has been written in Python, and is part of current Slicer3 release.<br />
<br />
Functionality of Python Stochastic Tractography module in Slicer 3.0 includes:<br />
*Preprocessing:<br />
<br />
Reading DWI and ROI files in nhdr format<br />
<br />
Smoothing DWI data<br />
<br />
Creating brain and white mater masks<br />
<br />
Removing artifacts in WM masks by comparing this mask with FA map<br />
<br />
Producing diffusion indices (FA, Mode, Trace)<br />
<br />
*Creating probability maps (parameters involve number of tracts per seed, tract length, step size, stopping criteria)<br />
<br />
Probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
;* Parameters/steps that need to be adjusted using someone else's data<br />
Software was tested on 3 sets of data (1.5T anisotropic Siemens, 3T anisotropic GE, 3T isotropic GE), and works for all of them without major modifications.<br />
<br />
==Software & documentation==<br />
* Download Slicer 3.0 [http://www.slicer.org/pages/Downloads here].<br />
<br />
:* Module documentation can be found here:<br />
:**[[Media:IJdata.tar.gz|Training Dataset]]<br />
:**[[Media:PNLStochasictNew.ppt|Training Presentation]]<br />
:**[[Media:Helix.zip|Sample Helix Dataset]]<br />
<br />
:* Software that you will also need to launch:<br />
:**[http://www.python.org/download/releases/2.5.4/ Python 2.5 Software]<br />
:**[http://sourceforge.net/project/showfiles.php?group_id=1369&package_id=175103 NumPy Software]<br />
:**[http://pnl.bwh.harvard.edu/NAMIC/Slicer3-build.zip Slicer 3 Build for Windows]<br />
:**[http://pnl.bwh.harvard.edu/NAMIC/Slicer3-lib.zip Slicer 3 Lib for Windows]<br />
<br />
==Team== <br />
[[Image:PnllogoworkNEW.png|left|50px]]<br />
DBP: Marek Kubicki, MD, PhD, Sylvain Bouix, PhD, Julien De Siebenthal, PhD, Doug Terry, BS <br />
PNL, Department of Psychiatry, BWH, Harvard Medical School, Boston, MA<br />
<br />
<br />
<br />
<br />
[[Image:csail.jpg|left|50px]]<br />
Core 1: Polina Golland, MIT <br />
<br />
<br />
<br />
<br />
[[Image: Kitware.png|left|50px]]<br />
Core 2: Brad Davis, Steve Pieper, Kitware<br />
<br />
==Outreach==<br />
;* Visit our [http://pnl.bwh.harvard.edu/pub/all.html publication database.]<br />
;* Planned outreach activities (including presentations, tutorials/workshops) at conferences<br />
:1. Method of stochastic tractography, along with the module functionality will be presented at the DTI symposium during World Biological Psychiatry Symposium in Florence, Italy in April 2009. <br />
:2. Clinical applications, including results of arcuate fasciculus study in schizophrenia will be presented at the Biological Psychiatry Congress in Vancouver, Canada in May 2009.</div>Kubickihttps://www.na-mic.org/w/index.php?title=AHM2009:PNL&diff=34475AHM2009:PNL2009-01-07T22:12:55Z<p>Kubicki: /* Detailed Information about the Pipeline */</p>
<hr />
<div>[[AHM_2009#Agenda|Back to AHM 2009 Agenda]]<br />
<br />
__NOTOC__<br />
==PNL Roadmap Project==<br />
<br />
{|<br />
|[[Image:Helix.png|thumb|280x150px|Stochastic Tractography on helix phantom]]<br />
|[[Image:Wmm.png|thumb|280x150px|White Matter Mask generated from phantom]]<br />
|[[Image:STArcuate.jpg|thumb|280x150px|Stochastic Tractography of Arcuate Fasciculus]]<br />
|[[Image:Step1.png|thumb|380x250px|Stochastic Tractography Module]]<br />
|[[Image:Step2.png|thumb|180x120px|Stochastic Tractography Module]]<br />
<br />
|}<br />
<br />
== Overview ==<br />
;* What problem does the pipeline solve?<br />
Most tractography methods (Deterministic/Principal Diffusion Direction) estimate fibers by tracing the maximum direction of diffusion. <br />
<br />
A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. <br />
<br />
Stochastic tractography attempts to quantify the uncertainty associated with estimated fibers, by using a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths.<br />
<br />
Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. <br />
;* Who is the targeted user?<br />
Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. <br />
Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). <br />
;* How does the pipeline compare to state of the art?<br />
To date, there are two popular toolboxes that include probabilistic/stochastic tractography modules. Functionality of both modules (FSL and PICO) is limited, and software is not open source.<br />
<br />
==Detailed Information about the Pipeline==<br />
Stochastic Tractography pipeline has been written in Python, and is part of current Slicer3 release.<br />
<br />
Functionality of Python Stochastic Tractography module in Slicer 3.0 includes:<br />
*Preprocessing:<br />
<br />
Reading DWI and ROI files in nhdr format<br />
<br />
Smoothing DWI data<br />
<br />
Creating brain and white mater masks<br />
<br />
Removing artifacts in WM masks by comparing this mask with FA map<br />
<br />
Producing diffusion indices (FA, Mode, Trace)<br />
<br />
*Creating probability maps (parameters involve number of tracts per seed, tract length, step size, stopping criteria)<br />
<br />
Probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
;* Parameters/steps that need to be adjusted using someone else's data<br />
Software was tested on 3 sets of data (1.5T anisotropic Siemens, 3T anisotropic GE, 3T isotropic GE), and works for all of them without major modifications.<br />
<br />
==Software & documentation==<br />
* Download Slicer 3.0 [http://www.slicer.org/pages/Downloads here].<br />
*[[Media:IJdata.tar.gz|Training Dataset]]<br />
*[[Media:Slicer3STModule.ppt|Training Presentation]]<br />
*[[Media:SlicerSTtutorial.ogg|Training Webcast]] ([http://www.theora.org Theora format]) <br />
<br />
==Team== <br />
[[Image:PnllogoworkNEW.png|left|50px]]<br />
DBP: Marek Kubicki, MD, PhD, Sylvain Bouix, PhD, Julien De Siebenthal, PhD, Doug Terry, BS <br />
PNL, Department of Psychiatry, BWH, Harvard Medical School, Boston, MA<br />
<br />
<br />
<br />
<br />
[[Image:csail.jpg|left|50px]]<br />
Core 1: Polina Gollard, MIT <br />
<br />
<br />
<br />
<br />
[[Image: Kitware.png|left|50px]]<br />
Core 2: Brad Davis, Steve Pieper, Kitware<br />
<br />
==Outreach==<br />
;* Visit our [http://pnl.bwh.harvard.edu/pub/all.html publication database.]<br />
;* Planned outreach activities (including presentations, tutorials/workshops) at conferences<br />
:1. Method of stochastic tractography, along with the module functionality will be presented at the DTI symposium during World Biological Psychiatry Symposium in Florence, Italy in April 2009. <br />
:2. Clinical applications, including results of arcuate fasciculus study in schizophrenia will be presented at the Biological Psychiatry Congress in Vancouver, Canada in May 2009.</div>Kubickihttps://www.na-mic.org/w/index.php?title=AHM2009:PNL&diff=34469AHM2009:PNL2009-01-07T22:09:55Z<p>Kubicki: /* Overview */</p>
<hr />
<div>[[AHM_2009#Agenda|Back to AHM 2009 Agenda]]<br />
<br />
__NOTOC__<br />
==PNL Roadmap Project==<br />
<br />
{|<br />
|[[Image:Helix.png|thumb|280x150px|Stochastic Tractography on helix phantom]]<br />
|[[Image:Wmm.png|thumb|280x150px|White Matter Mask generated from phantom]]<br />
|[[Image:STArcuate.jpg|thumb|280x150px|Stochastic Tractography of Arcuate Fasciculus]]<br />
|[[Image:Step1.png|thumb|380x250px|Stochastic Tractography Module]]<br />
|[[Image:Step2.png|thumb|180x120px|Stochastic Tractography Module]]<br />
<br />
|}<br />
<br />
== Overview ==<br />
;* What problem does the pipeline solve?<br />
Most tractography methods (Deterministic/Principal Diffusion Direction) estimate fibers by tracing the maximum direction of diffusion. <br />
<br />
A limitation of this approach is that, in practice, several factors introduce uncertainty in the tracking procedure, including, noise, splitting and crossing fibers, head motion and image artifacts. <br />
<br />
Stochastic tractography attempts to quantify the uncertainty associated with estimated fibers, by using a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths.<br />
<br />
Based on probability functions, using a sequential importance sampling technique ([http://lmi.bwh.harvard.edu/papers/pdfs/2002/bjornemoMICCAI02.pdf Bjornemo et al., 2002]), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations. <br />
;* Who is the targeted user?<br />
Since diffusion direction uncertainty within the gray matter is quite significant; principal diffusion direction approaches usually do not work for tracking between two gray matter regions. Thus if one requires finding connections between a priori selected anatomical gray matter regions, defined either by anatomical segmentations (in case of using structural ROI data), or functional activations (in case of megring DTI with fMRI), stochastic tractography seems to be the method of choice. <br />
Stochastic Tractography is also comparable, if not better, in defining large white matter fiber bundles, especially those traveling through white matter regions characterized by increased diffusion uncertainty (fiber crossings). <br />
;* How does the pipeline compare to state of the art?<br />
To date, there are two popular toolboxes that include probabilistic/stochastic tractography modules. Functionality of both modules (FSL and PICO) is limited, and software is not open source.<br />
<br />
==Detailed Information about the Pipeline==<br />
Stochastic Tractography pipeline has been written in Python, and is part of current Slicer3 release.<br />
<br />
Functionality of Python Stochastic Tractography module in Slicer 3.0 includes:<br />
*Preprocessing:<br />
Reading DWI and ROI files in nhdr format<br />
Smoothing DWI data<br />
Creating brain and white mater masks<br />
Removing artifacts in WM masks by comparing this mask with FA map<br />
Producing diffusion indices (FA, Mode, Trace)<br />
*Creating probability maps (parameters involve number of tracts per seed, tract length, step size, stopping criteria)<br />
<br />
Probability maps can be saved as ROIs, and either used directly, or thresholded (at certain probability, step claimed by few publications to remove noise) in slicer to mask and compute average FA, Mode, Trace for entire connection. Diffusion indices can be also weighted by the probability of connection for each voxel.<br />
<br />
;* Parameters/steps that need to be adjusted using someone else's data<br />
Software was tested on 3 sets of data (1.5T anisotropic Siemens, 3T anisotropic GE, 3T isotropic GE), and works for all of them without major modifications.<br />
<br />
==Software & documentation==<br />
* Download Slicer 3.0 [http://www.slicer.org/pages/Downloads here].<br />
*[[Media:IJdata.tar.gz|Training Dataset]]<br />
*[[Media:Slicer3STModule.ppt|Training Presentation]]<br />
*[[Media:SlicerSTtutorial.ogg|Training Webcast]] ([http://www.theora.org Theora format]) <br />
<br />
==Team== <br />
[[Image:PnllogoworkNEW.png|left|50px]]<br />
DBP: Marek Kubicki, MD, PhD, Sylvain Bouix, PhD, Julien De Siebenthal, PhD, Doug Terry, BS <br />
PNL, Department of Psychiatry, BWH, Harvard Medical School, Boston, MA<br />
<br />
<br />
<br />
<br />
[[Image:csail.jpg|left|50px]]<br />
Core 1: Polina Gollard, MIT <br />
<br />
<br />
<br />
<br />
[[Image: Kitware.png|left|50px]]<br />
Core 2: Brad Davis, Steve Pieper, Kitware<br />
<br />
==Outreach==<br />
;* Visit our [http://pnl.bwh.harvard.edu/pub/all.html publication database.]<br />
;* Planned outreach activities (including presentations, tutorials/workshops) at conferences<br />
:1. Method of stochastic tractography, along with the module functionality will be presented at the DTI symposium during World Biological Psychiatry Symposium in Florence, Italy in April 2009. <br />
:2. Clinical applications, including results of arcuate fasciculus study in schizophrenia will be presented at the Biological Psychiatry Congress in Vancouver, Canada in May 2009.</div>Kubicki