NAMIC Wiki - User contributions [en]

Renewal-06-2013

2013-05-15T14:34:59Z

Sylvain:

[[2013_Summer_Project_Week#Agenda|Back to Summer project week Agenda]]

Closed Door Session with Ron. Room [[MIT_Project_Week_Rooms#32-D407|32-D407]]

=Agenda=
Getting together in one room will allow us to revisit the overall concept, to fine tune our approach, and to discuss open questions with respect to the grant as a whole.
=Participants=
TRD PI's and their delegates.
*Ron Kikinis
*Polina Golland (by google hangout)
*Allen Tannenbaum
*Sonia Pujol
*Steve Pieper
*Jim Miller
*Sylvain Bouix

DBP2:Harvard:Brain Segmentation Roadmap

2010-11-02T20:24:20Z

Sylvain: /* Related Clinical Projects */

Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]
__NOTOC__
=Stochastic Tractography for VCFS=
== Roadmap ==

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.

== Algorithm ==

[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]
; A-Description
* 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)

[[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>]]
; B-Possible Applications
* 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).

* 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)

[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]
; C-References

* [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.]
* [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]

[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]

==Module==
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY
;Functionality of Python Stochastic Tractography module in Slicer 3.0
* IO:
Module reads files (DWI and ROIs) in nhdr format.
* Smoothing:
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.
* Brain Mask:
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.
*IJK/RAS Switch
Chooses the way the nhdrs are read.
*Diffusion Tensor:
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)
*Tractography:
Parameters that need to be adjusted:
: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).
: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
: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."
: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).
:5. Use Basic Method: switches between Friman and McGraw algorithms.
*Connectivity Map:
This step creates output probability maps.
:1. binary: each voxel is counted only once if at least one fiber pass through it
:2. cumulative: tracts are summed by voxel independently
:3. weighted: tracts are summed by voxel depending on their length
*Length Based:
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.
*Threshold
This step will reject tracts whose endpoints are lower than the threshold value.
*Spherical ROI vicinity
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.
*Vicinity
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.

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.

:* Module documentation and training data can be found here:
:**[[Slicer3:Stochastic_tractography]]

==Work Accomplished==
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]
; A - Optimization and testing of stochastic tractography algorithm :
:* 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]).
:* 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]).
:* 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).
:* 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.
:* Module have been tested on other Philips and GE datasets.

; B - Clinical Applications

:* 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).
:* 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)

== Related Clinical Projects ==

;* Arcuate Fasciculus Extraction and Analysis in Schizophrenia
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]
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.
:Project involves:
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri).
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high.
:* Non-linear registration of labelmaps to the DTI space.
:* Seeding tracts within both ROIs.
:* 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]].
:* 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.

[[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>]]

;* Semantic Network Connectivity in Schizophrenia
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.
:Project involves:
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia
:* Analysis of functional connectivity (using FSL) between nodes of semantic network
:* Whole brain Voxel Based analysis of DTI data in same population
:* Use of stochastic tractography to identify connections between functional nodes
:* Correlational analysis involving anatomical and functional connectivity data.
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.
:* <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

[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]

;* Anatomical Connectivity Abnormalities in VCFS
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.
:Project involves:
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.
:* Extracting paths of interest, and calculating FA along the paths for group comparison.
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data.
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010).

[[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>]]

;* Study of Default Network in Psychosis
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.
:* Results presentation and paper submission. We are in the process of analyzing results.

;* Study of OFC-ACC connectivity in Chronic Schizophrenia
We are using stochastic tractography to examine white matter connectivity between medial anterior and posterior OFC and rostral ACC (cognitive part of ACC).
:* 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.

[[Image:Otani-NAMIC.png|thumb|right|200px|<font size=1>Figure 11: Stochastic density map (green) between OFC (red )and ACC (purple).</font>]]

;* Study of thalamus segmentation based on cortical connectivity in Chronic Schizophrenia
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.
:* Results presentation and paper submission. Data analysis and paper in preparation.

[[Image:Taiga-NAMIC.png|thumb|right|400px|<font size=1>Figure 12: Thalamus parcellation into discrete ROIs based on its connectivity to 10 cortical ROIs using stochastic tracking.</font>]]

;* Tractography Comparison Project
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.

; - Related References

:* 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.
:* 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]
:* 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]
:* 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.
:* 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.
:* 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.
:* 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.

===Staffing Plan===

* Sylvain and Ryan are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
* Andrew Rauch is our NAMIC RA.
* 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.
* Polina is the algorithm core contact
* Brad is the engineering core contact

===Schedule===

* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data.
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom.
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data.
* '''11/2008''' - Slicer 3 module prototype using python.
* '''12/2008''' - Slicer 3 module official release
* '''12/2008''' - Documentation and packaging for dissemination.
* '''12/2008''' - First clinical application of stochastic tractography module.
* '''01/2009''' - First draft of the clinical paper.
* '''05/2009''' - Distortion correction and nonlinear registration added to the module
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline.
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.
* '''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.
* '''01/2010''' - AHM progress presentation.
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco.
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release.

===Team and Institute===
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo
*NA-MIC Engineering Contact: Brad Davis, Kitware
*NA-MIC Algorithms Contact: Polina Gollard, MIT

===Publications===

''In print''

*[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]

*[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]

[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]

File:Taiga-NAMIC.png

2010-11-02T20:22:27Z

Sylvain: uploaded a new version of "File:Taiga-NAMIC.png"

File:Taiga-NAMIC.png

2010-11-02T20:21:47Z

Sylvain:

DBP2:Harvard:Brain Segmentation Roadmap

2010-11-02T20:18:52Z

Sylvain: /* Related Clinical Projects */

Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]
__NOTOC__
=Stochastic Tractography for VCFS=
== Roadmap ==

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.

== Algorithm ==

[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]
; A-Description
* 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)

[[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>]]
; B-Possible Applications
* 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).

* 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)

[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]
; C-References

* [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.]
* [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]

[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]

==Module==
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY
;Functionality of Python Stochastic Tractography module in Slicer 3.0
* IO:
Module reads files (DWI and ROIs) in nhdr format.
* Smoothing:
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.
* Brain Mask:
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.
*IJK/RAS Switch
Chooses the way the nhdrs are read.
*Diffusion Tensor:
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)
*Tractography:
Parameters that need to be adjusted:
: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).
: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
: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."
: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).
:5. Use Basic Method: switches between Friman and McGraw algorithms.
*Connectivity Map:
This step creates output probability maps.
:1. binary: each voxel is counted only once if at least one fiber pass through it
:2. cumulative: tracts are summed by voxel independently
:3. weighted: tracts are summed by voxel depending on their length
*Length Based:
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.
*Threshold
This step will reject tracts whose endpoints are lower than the threshold value.
*Spherical ROI vicinity
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.
*Vicinity
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.

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.

:* Module documentation and training data can be found here:
:**[[Slicer3:Stochastic_tractography]]

==Work Accomplished==
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]
; A - Optimization and testing of stochastic tractography algorithm :
:* 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]).
:* 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]).
:* 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).
:* 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.
:* Module have been tested on other Philips and GE datasets.

; B - Clinical Applications

:* 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).
:* 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)

== Related Clinical Projects ==

;* Arcuate Fasciculus Extraction and Analysis in Schizophrenia
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]
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.
:Project involves:
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri).
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high.
:* Non-linear registration of labelmaps to the DTI space.
:* Seeding tracts within both ROIs.
:* 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]].
:* 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.

[[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>]]

;* Semantic Network Connectivity in Schizophrenia
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.
:Project involves:
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia
:* Analysis of functional connectivity (using FSL) between nodes of semantic network
:* Whole brain Voxel Based analysis of DTI data in same population
:* Use of stochastic tractography to identify connections between functional nodes
:* Correlational analysis involving anatomical and functional connectivity data.
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.
:* <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

[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]

;* Anatomical Connectivity Abnormalities in VCFS
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.
:Project involves:
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.
:* Extracting paths of interest, and calculating FA along the paths for group comparison.
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data.
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010).

[[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>]]

;* Study of Default Network in Psychosis
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.
:* Results presentation and paper submission. We are in the process of analyzing results.

;* Study of OFC-ACC connectivity in Chronic Schizophrenia
We are using stochastic tractography to examine white matter connectivity between medial anterior and posterior OFC and rostral ACC (cognitive part of ACC).
:* 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.

[[Image:Otani-NAMIC.png|thumb|right|200px|<font size=1>Figure 11: Stochastic density map (green) between OFC (red )and ACC (purple).</font>]]

;* Study of thalamus segmentation based on cortical connectivity in Chronic Schizophrenia
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.
:* Results presentation and paper submission. Data analysis and paper in preparation.

;* Tractography Comparison Project
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.

; - Related References

:* 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.
:* 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]
:* 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]
:* 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.
:* 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.
:* 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.
:* 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.

===Staffing Plan===

* Sylvain and Ryan are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
* Andrew Rauch is our NAMIC RA.
* 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.
* Polina is the algorithm core contact
* Brad is the engineering core contact

===Schedule===

* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data.
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom.
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data.
* '''11/2008''' - Slicer 3 module prototype using python.
* '''12/2008''' - Slicer 3 module official release
* '''12/2008''' - Documentation and packaging for dissemination.
* '''12/2008''' - First clinical application of stochastic tractography module.
* '''01/2009''' - First draft of the clinical paper.
* '''05/2009''' - Distortion correction and nonlinear registration added to the module
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline.
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.
* '''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.
* '''01/2010''' - AHM progress presentation.
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco.
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release.

===Team and Institute===
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo
*NA-MIC Engineering Contact: Brad Davis, Kitware
*NA-MIC Algorithms Contact: Polina Gollard, MIT

===Publications===

''In print''

*[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]

*[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]

[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]

File:Otani-NAMIC.png

2010-11-02T20:17:19Z

Sylvain:

DBP2:Harvard:Brain Segmentation Roadmap

2010-11-02T19:14:30Z

Sylvain: /* Module */

Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]
__NOTOC__
=Stochastic Tractography for VCFS=
== Roadmap ==

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.

== Algorithm ==

[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]
; A-Description
* 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)

[[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>]]
; B-Possible Applications
* 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).

* 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)

[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]
; C-References

* [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.]
* [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]

[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]

==Module==
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY
;Functionality of Python Stochastic Tractography module in Slicer 3.0
* IO:
Module reads files (DWI and ROIs) in nhdr format.
* Smoothing:
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.
* Brain Mask:
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.
*IJK/RAS Switch
Chooses the way the nhdrs are read.
*Diffusion Tensor:
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)
*Tractography:
Parameters that need to be adjusted:
: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).
: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
: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."
: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).
:5. Use Basic Method: switches between Friman and McGraw algorithms.
*Connectivity Map:
This step creates output probability maps.
:1. binary: each voxel is counted only once if at least one fiber pass through it
:2. cumulative: tracts are summed by voxel independently
:3. weighted: tracts are summed by voxel depending on their length
*Length Based:
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.
*Threshold
This step will reject tracts whose endpoints are lower than the threshold value.
*Spherical ROI vicinity
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.
*Vicinity
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.

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.

:* Module documentation and training data can be found here:
:**[[Slicer3:Stochastic_tractography]]

==Work Accomplished==
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]
; A - Optimization and testing of stochastic tractography algorithm :
:* 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]).
:* 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]).
:* 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).
:* 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.
:* Module have been tested on other Philips and GE datasets.

; B - Clinical Applications

:* 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).
:* 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)

== Related Clinical Projects ==

;* Arcuate Fasciculus Extraction and Analysis in Schizophrenia
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]
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.
:Project involves:
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri).
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high.
:* Non-linear registration of labelmaps to the DTI space.
:* Seeding tracts within both ROIs.
:* 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]].
:* 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.

[[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>]]

;* Semantic Network Connectivity in Schizophrenia
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.
:Project involves:
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia
:* Analysis of functional connectivity (using FSL) between nodes of semantic network
:* Whole brain Voxel Based analysis of DTI data in same population
:* Use of stochastic tractography to identify connections between functional nodes
:* Correlational analysis involving anatomical and functional connectivity data.
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.
:* <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

[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]

;* Anatomical Connectivity Abnormalities in VCFS
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.
:Project involves:
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.
:* Extracting paths of interest, and calculating FA along the paths for group comparison.
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data.
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010).

[[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>]]

;* Study of Default Network in Psychosis
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.
:* Results presentation and paper submission. We are in the process of analyzing results.

;* Study of OFC-ACC connectivity in Chronic Schizophrenia
We are using stochastic tractography to examine white matter connectivity between medial anterior and posterior OFC and rostral ACC (cognitive part of ACC).
:* 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.

;* Study of thalamus segmentation based on cortical connectivity in Chronic Schizophrenia
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.
:* Results presentation and paper submission. Data analysis and paper in preparation.

;* Tractography Comparison Project
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.

; - Related References

:* 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.
:* 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]
:* 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]
:* 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.
:* 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.
:* 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.
:* 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.

===Staffing Plan===

* Sylvain and Ryan are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
* Andrew Rauch is our NAMIC RA.
* 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.
* Polina is the algorithm core contact
* Brad is the engineering core contact

===Schedule===

* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data.
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom.
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data.
* '''11/2008''' - Slicer 3 module prototype using python.
* '''12/2008''' - Slicer 3 module official release
* '''12/2008''' - Documentation and packaging for dissemination.
* '''12/2008''' - First clinical application of stochastic tractography module.
* '''01/2009''' - First draft of the clinical paper.
* '''05/2009''' - Distortion correction and nonlinear registration added to the module
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline.
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.
* '''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.
* '''01/2010''' - AHM progress presentation.
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco.
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release.

===Team and Institute===
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo
*NA-MIC Engineering Contact: Brad Davis, Kitware
*NA-MIC Algorithms Contact: Polina Gollard, MIT

===Publications===

''In print''

*[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]

*[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]

[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]

DBP2:Harvard:Brain Segmentation Roadmap

2010-11-02T19:14:14Z

Sylvain: /* Module */

Back to [[NA-MIC_Internal_Collaborations|NA-MIC Collaborations]], [[DBP2:Harvard|Harvard DBP 2]]
__NOTOC__
=Stochastic Tractography for VCFS=
== Roadmap ==

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.

== Algorithm ==

[[Image:IC_sto_new.png|thumb|right|200px|<font size=1> Figure 1: Comparison of deterministic and stochastic tractography algorithms</font>]]
; A-Description
* 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)

[[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>]]
; B-Possible Applications
* 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).

* 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)

[[Image:IC-comp-new.png|thumb|right|200px|<font size=1>Figure 3: Streamline vs. stochastic tractography of the Internal Capsule</font>]]
; C-References

* [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.]
* [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]

[[Image:StochasticGUI1.PNG|thumb|right|200px|<font size=1> Figure 4: Python Stochastic Tractography GUI </font>]]

==Module==
Can be found in: MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY
;Functionality of Python Stochastic Tractography module in Slicer 3.0
* IO:
Module reads files (DWI and ROIs) in nhdr format.
* Smoothing:
One can smooth the DWI data (only Gausian smoothing is supported at this time). We recommend it if the data is noisy.
* Brain Mask:
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.
*IJK/RAS Switch
Chooses the way the nhdrs are read.
*Diffusion Tensor:
This step allows output of the tensor image and can output anisotropy indices (FA/Mode/Trace)
*Tractography:
Parameters that need to be adjusted:
: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).
: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
: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."
: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).
:5. Use Basic Method: switches between Friman and McGraw algorithms.
*Connectivity Map:
This step creates output probability maps.
:1. binary: each voxel is counted only once if at least one fiber pass through it
:2. cumulative: tracts are summed by voxel independently
:3. weighted: tracts are summed by voxel depending on their length
*Length Based:
This step will output only either the shortest 1/3, middle 1/3, or longest 1/3 of the tracts.
*Threshold
This step will reject tracts whose endpoints are lower than the threshold value.
*Spherical ROI vicinity
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.
*Vicinity
This step traces back n number of steps from tract endpoint to check if track crosses target ROI. If so, tract is included.

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.

:* Module documentation and training data can be found here:
:**[[Slicer3:Stochastic_tractography]]]

==Work Accomplished==
[[Image:helix_withsmoothing.png|thumb|right|200px|<font size=1>Figure 6: Stochastic tractography from a single ROI on helix phantom</font>]]
; A - Optimization and testing of stochastic tractography algorithm :
:* 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]).
:* 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]).
:* 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).
:* 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.
:* Module have been tested on other Philips and GE datasets.

; B - Clinical Applications

:* 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).
:* 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)

== Related Clinical Projects ==

;* Arcuate Fasciculus Extraction and Analysis in Schizophrenia
[[Image:STArcuate.jpg|thumb|right|200px|<font size=1>Figure 7: The arcuate fasciculus including seed, midpoint and target ROI's.</font>]]
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.
:Project involves:
:* Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Superior Temporal Gyri).
:* White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high.
:* Non-linear registration of labelmaps to the DTI space.
:* Seeding tracts within both ROIs.
:* 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]].
:* 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.

[[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>]]

;* Semantic Network Connectivity in Schizophrenia
We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.
:Project involves:
:* fMRI data analysis and identification of functional nodes involved in semantic processing in healthy controls and seubjects with schizophrenia
:* Analysis of functional connectivity (using FSL) between nodes of semantic network
:* Whole brain Voxel Based analysis of DTI data in same population
:* Use of stochastic tractography to identify connections between functional nodes
:* Correlational analysis involving anatomical and functional connectivity data.
:* Results presentation and paper submission. Data was presented at HBM conference in 2007, paper has been accepted for publication at HBM.
:* <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

[[Image:ScreenshotFreeSurferDeepMatterSagitalView-vcase1-2009-06-12.jpg|thumb|right|200px|<font size=1>Figure 9: Brain automatic segmentation of subject with VCFS.</font>]]

;* Anatomical Connectivity Abnormalities in VCFS
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.
:Project involves:
:* Whole brain, automatic segmentation of brain images obtained from patients with VCFS in order to identify structures involved in this disease.
:* Registration between anatomical and DTI scans (manual skull stripping followed by linear followed by nonlinear registration of SPGRs to DTI space).
:* Use of stochastic tractography to identify connections between gray matter regions identified in the disease.
:* Extracting paths of interest, and calculating FA along the paths for group comparison.
:* Correlational analysis involving anatomical and connectivity data, clinical information and genetic data.
:* Results presentation and paper submission. We plan to submit an abstract with study results for Biological Psychiatry symposium (dedline December 2010).

[[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>]]

;* Study of Default Network in Psychosis
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.
:* Results presentation and paper submission. We are in the process of analyzing results.

;* Study of OFC-ACC connectivity in Chronic Schizophrenia
We are using stochastic tractography to examine white matter connectivity between medial anterior and posterior OFC and rostral ACC (cognitive part of ACC).
:* 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.

;* Study of thalamus segmentation based on cortical connectivity in Chronic Schizophrenia
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.
:* Results presentation and paper submission. Data analysis and paper in preparation.

;* Tractography Comparison Project
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.

; - Related References

:* 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.
:* 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]
:* 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]
:* 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.
:* 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.
:* 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.
:* 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.

===Staffing Plan===

* Sylvain and Ryan are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
* Andrew Rauch is our NAMIC RA.
* 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.
* Polina is the algorithm core contact
* Brad is the engineering core contact

===Schedule===

* '''10/2007''' - Optimization of Stochastic Tractography algorythm for 1.5T data.
* '''10/2007''' - Algorythm testing on Santa Fe data set and diffusion phantom.
* '''06/2008''' - Optimization of Stochastic Tractography algorythm for 3T data.
* '''11/2008''' - Slicer 3 module prototype using python.
* '''12/2008''' - Slicer 3 module official release
* '''12/2008''' - Documentation and packaging for dissemination.
* '''12/2008''' - First clinical application of stochastic tractography module.
* '''01/2009''' - First draft of the clinical paper.
* '''05/2009''' - Distortion correction and nonlinear registration added to the module
* '''05/2009''' - Symposium on tractography, including stochastic methods at World Biological Psychiatry Symposium in Florence, Italy.
* '''05/2009''' - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Florence, Italy.
* '''07/2009''' - Summer Programming week- work on optimizing and speeding up data processing, releasing second generation of software that includes preprocessing pipeline.
* '''07/2009''' - Continue working on clinical collaborative studies using stochastic tractography module.
* '''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.
* '''01/2010''' - AHM progress presentation.
* '''05/2010''' - Presentation of clinical findings at Annual Biological Psychiatry Symposium, San Francisco.
* '''07/2010''' - Summer Programming week- presentation of new software manual, software included in new slicer 3 release.

===Team and Institute===
*PI: Marek Kubicki (kubicki at bwh.harvard.edu)
*DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Ryan Ecbo
*NA-MIC Engineering Contact: Brad Davis, Kitware
*NA-MIC Algorithms Contact: Polina Gollard, MIT

===Publications===

''In print''

*[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]

*[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]

[[Category: Schizophrenia]] [[Category: Diffusion MRI]] [[Category: Segmentation]]

2010 Summer Project Week Shape Test Bench

2010-06-16T14:25:32Z

Sylvain: Created page with '__NOTOC__ <gallery> Image:PW-MIT2010.png|Projects List Image:Caudate_orig.png|Original Caudate Shape. Image:Caudate_bump.png|Artifical Bump …'

__NOTOC__
<gallery>
Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]
Image:Caudate_orig.png|Original Caudate Shape.
Image:Caudate_bump.png|Artifical Bump Added to the Tail.
</gallery>

==Key Investigators==
* UNC: Marc Niethammer
* BWH: Sylvain Bouix

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
* A prototype software to create population of synthetic 3D objects for shape analysis testing and evaluation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
* Artifical shape changes in the form of: global volume preserving transforms, "dimples and bulges", boundary noise.
* Automatic population creation mimicking real data variability.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
* First version of boundary noise creation done.
* Affine transforms for population creation

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

To early to tell. Either a downloadable set of images, or an open source software suite.

==References==

</div>

2010 Summer Project Week

2010-06-16T14:24:31Z

Sylvain: /* Shape Analysis */

__NOTOC__

Back to [[Project Events]], [[Events]]

[[Image:PW-MIT2010.png|300px]]

==Background==

We are pleased to announce the 11th PROJECT WEEK of hands-on research and development activity for applications in Image-Guided Therapy, Neuroscience, and several additional areas of biomedical research that enable personalized medicine. 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.

Active preparation begins on Thursday, April 15th 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.

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.

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.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Logistics ==
*'''Dates:''' June 21-25, 2010
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''REGISTRATION:''' Please click [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 here] to do an on-line registration for the meeting that will allow you to pay by credit card, or send a check.
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week).
*'''Hotel:''' We have reserved a block of rooms at the Boston Marriott Cambridge Hotel, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. (Phone: 617.252.4405, Fax: 617.494.6565) [http://www.marriott.com/hotels/travel/BOSCB?groupCode=NAMNAMA&app=resvlink&fromDate=6/20/10&toDate=6/25/10 Please click here to reserve.] You will be directed to the property's home page with the group code already entered in the appropriate field. All you need to do is enter your arrival date to begin the reservation process.

''' All reservations must be made by Tuesday, June 1, 2010 to receive the discounted rate of'''
''' $189/night/room (plus tax).'''
''' This rate is good only through June 1.'''

Please note that if you try to reserve a room outside of the block on the shoulder nights via the link, you will be told that the group rate is not available for the duration of your stay. To reserve those rooms, which might not be at the group rate because it is based upon availability, please call Marriott Central Reservations at 1-800-228-9290.

*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.
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]

==Agenda==
=== Monday, June 21, 2010 ===
** noon-1pm lunch
**1pm: Welcome (Ron Kikinis)
** 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])
** 3:30-5:30pm Tutorial: [[2010 Summer Project Week Breakout: Getting Started with Qt]] (Adam Weinrich, Nokia)

=== Tuesday, June 22, 2010 ===
** 8:30am breakfast
**9-9:45am: NA-MIC Kit Update (Jim Miller) - include Module nomenclature (Extensions: cmdline vs loadable, Built-in), QT, Include Superbuild demo by Dave P.
**9:45-10:30am 3D Slicer Update (Steve Pieper)
**10:30-11am OpenIGTLink Update (Junichi Tokuda)
**11-12pm: Slicer Hands-on Workshop (Randy Gollub, Sonia Pujol)
** noon lunch
** 1-3pm: Breakout Session: QT/Slicer (Steve, JC, J2) (w/ possible QnA with QT experts)
** 3pm: [[Summer_2010_Tutorial_Contest|Tutorial Contest Presentations]]
** 4-5pm [[2010 Summer Project Week Breakout Session: Data Management]] (Dan Marcus, Stephen Aylward)
** 5:30pm adjourn for day

=== Wednesday, June 23, 2010 ===
** 8:30am breakfast
** 9am-12pm Breakout Session: [[2010 Project Week Breakout Session: ITK]] (Luis Ibanez)
** noon lunch
**12:45pm: [[Events:TutorialContestJune2010|Tutorial Contest Winner Announcement]]
**1-3pm: Breakout Session: [[Microscopy_Image_Analysis]] (Sean Megason)
**3-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:QA Training]] (Luis Ibanez)
**3-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:VTK Widget]] (Nicole, Kilian, JC)
** 5:30pm adjourn for day

=== Thursday, June 24, 2010 ===
** 8:30am breakfast

** 9am-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]
** noon lunch
** 1-2pm: [[2010 Summer Project Week Breakout Session:GWE]] (Marco Ruiz)
** 2-2:30pm: [http://www.commontk.org/index.php/Build_Instructions#Simple_Git Simple Git] (Steve Pieper)
** 5:30pm adjourn for day

=== Friday, June 25, 2010 ===
** 8:30am breakfast
** 10am-noon: [[#Projects|Project Progress Updates]]
*** Noon: Lunch boxes and adjourn by 1:30pm.
***We need to empty room by 1:30. You are welcome to use wireless in Stata.
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]
***Next Project Week [[AHM_2011|in Utah]]

==Projects==

=== Segmentation ===
#[[2010_Summer_Project_Week_Robust_Statistics_Segmenter_Slicer_Module|Robust Statistics Segmenter Slicer Module]] (Yi Gao, Allen Tannenbaum, Ron Kikinis)
#[[2010_Summer_Project_Week_Multi_scale_Shape_Based_Segmentation_for_the_Hippocampus|Multi-scale Shape Based Segmentation for the Hippocampus]] (Yi Gao, Allen Tannenbaum)
#[[2010_Summer_Project_Week_SegmentationMeshEmbeddedContours|Segmentation on Mesh Surfaces Using Geometric Information]] (Peter Karasev, Karol Chudy, Allen Tannenbaum, GT; Ron Kikinis, BWH)
#[[2010_Summer_Project_Week/The Vascular Modeling Toolkit in 3D Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper, Ron Kikinis)
#[[2010_Summer_Project_Week_Prostate_MRI_Segmentation|Prostate Segmentation from MRI]] (Andriy Fedorov, Yi Gao)
#[[2010_Summer_Project_Week_SPECTRE|SPECTRE: Skull Stripping integration with Slicer]] (Nicole Aucoin, Min Chen)
#[[2010_Summer_Project_Week_White Matter Lesion segmentation|White Matter Lesion segmentation]] (Minjeong Kim, Xiaodong Tao, Jim Miller, Dinggang Shen)
#[[2010_Summer_Project_Week_Left ventricular scar segmentation| LV scar segmentation display and fusion]] (Dana C. Peters, Felix Liu, BIDMC, Boston)
#[[2010_Summer_Project_Week_EMSegmentation_kmeans|EMSegmentation: Automatic Intensity Initialization using KMeans ]](Priya Srinivasan, Daniel Haehn, Kilian Pohl, Sylvain Bouix)

=== Registration ===
#[[2010_Summer_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier)
#[[2010_Summer_Project_Week_Fiducial_Deformable_Registration|Fiducial-based deformable image registration]] (Nadya Shusharina, Greg Sharp)
#[[2010_Summer_Project_Week_HAMMER: Deformable Registration|HAMMER: Deformable Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller, Dinggang Shen)
#[[2010_Summer_Project_Week_Best_Regularization_Term_for_Demons_Registration_Algorithm|Best Regularization Term for Demons Registration Algorithm]] (Rui Li, Greg Sharp)
#[[2010_Summer_Project_Week_RegistrationEvaluation|Evaluation of Registration in Slicer]] (James Fishbaugh, Guido Gerig, Domink Meier)
#[[2010_Summer_Project_Week_MR_to_Ultrasound_Registration_Methodology|MR to Ultrasound Registration Methodology]] (Dieter Hahn, William Wells, Joachim Hornegger, Tina Kapur, Stephen Aylward)
#[[2010_Summer_Project_Week_Groupwise_Registration|Groupwise Registration]] (Ryan Eckbo, Jim Miller, Hans Johnson, Kilian Pohl, Daniel Haehn)

=== IGT ===
#[[2010_Summer_Project_Week_MR_to_CT_Registration_for_Prostate_Brachytherapy_Dose_Calculation|MR to CT Registration for Prostate Brachytherapy Dose Calculation]] (Andriy Fedorov, Dominik Meier, Hans Johnson)
#Intraoperative Brain Shift Monitoring Using Shear Model Transcranial Ultrasound (Jason White, Steve Pieper?, Pratik Patel?)
#Prostate Intervention(Junichi, Sam Song, Tamas Ungi)
# Liver Ablation (Haiying Liu)
# [[2010_Summer_Project_Week_BrainLab_Aurora_Hybrid_Navigation|BrainLab-Aurora Hybrid Navigation]] (Isaiah Norton, Dan Marcus, Noby Hata)
#[[2010_Summer_Project_Week_Dynamic_Image_Fusion_for_Guidance_of_Cardiac_Therapies|Dynamic Image Fusion for Guidance of Cardiac Therapies]] (Feng Li)
# [[2010_Summer_Project_Week_PerkStationModule|PerkStation Module]] (Tamas Ungi, Xiaodong Tao)
#[[2010_Summer_Project_Week_Co-registration_of_PET_and_DWI_Images_for_the_targeting_of_Glioma_Biopsies|Co-registration of PET and DWI Images for the targeting of Glioma Biopsies]] (Gareth Smith, Dominik Meir, Vince Magnotta)
#[[2010_Summer_Project_Week_Implementing_Open_IGT_Link_to_Virtual_Place_for_research_support|Implementing Open IGT Link to Virtual Place for research support]] (Nicholas Herlambang, Noby Hata)

=== Radiotherapy ===
#[[2010_Summer_Project_Week_DICOM_RT|Dicom RT plugin]] (Greg Sharp, Tamas Ungi)
#[[2010_Summer_Project_Week_HandN_Cancer|Adaptive Radiation Therapy for H&N cancer]] (Marta Peroni,Polina Golland,Greg Sharp)
#[[2010_Summer_Project_Week_Seg_Adapt_HNT|Segmentation for Adaptive Radiotherapy for Head, Neck, and Thorax]] (Ivan Kolesov, Greg Sharp, and Allen Tannenbaum )

=== Analysis ===
#Femoral Fracture Classification Brainstorming Session (Karl F, Vince M, Peter Karasev, Curt Lisle, Ron)
#[[2010_Summer_Project_Week_Cortical_Thickness_Analysis|Cortical thickness analysis]] (Clement Vachet, Heather Cody Hazlett, Martin Styner)
#[[2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface| MRSI module and SIVIC interface]] (B Menze, M Phothilimthana, J Crane (UCSF), B Olson (UCSF), P Golland)
#[[2010_Summer_Project_Week_Computer_Aided_Photodynamic_Therapy| Computer_Aided_Photodynamic_Therapy]] (E Pietka, D Spinczyk, P Szabelak)
#[[2010_NAMIC_Project_week:IA-FEMesh| IA-FEMesh Slicer3 Finite Meshing Module]] (N Grosland, V Magnotta, C Lisle, S Pieper)

===[[Microscopy Image Analysis]] ===
# [[ 2010 Project Week DICOM supplement 145 | DICOM supplement 145 ]] : Microscopy Image in the Dicom Standard (Mathieu Malaterre, Alex. Gouaillard)
# [[ 2010 Summer Project Week Microscopy extensions for ITK | Microscopy Extensions for ITK ]]: convolution, deconvolution, wavelets and more ( Gaetan Lemhann, Alex. Gouaillard )
# [[ 2010 Summer Project Week Flow Cytometry | Flow Cytometry ]] (Bertrand Moreau, Rossella Melchiotti, Alex. Gouaillard)
# [[Import/Export Farsight-GoFigure results]] (Lydie Souhait, Arnaud Gelas, Sean Megason, Badri Roysam)
# [[Farsight nuclear segmentation as GoFigure plugin]] (Arnaud Gelas, Sean Megason, Badri Roysam)
# [[ITK Spherical Harmonics filter for shape analysis of cell nuclei]] (Shantanu Singh, Arnaud Gelas, Sean Megason, Raghu Machiraju)
# [[ITK Analysis of Large Histology Datasets]] (Liya Ding, Kun Huang, Sean Megason, Raghu Machiraju)
# [[CTK Transfer function widget]] (Nicolas Rannou, Julien Finet, Stever Pieper)
# [[Seedings results comparison]] (Antonin Perrot-Audet, Kishore Mosaliganti, Badri Roysam, Sean Megason)
# [[ITK GPAC level set|ITK Multiphase and GPAC level sets]] (K. Palaniappan, Ilker Ersoy, Filiz Bunyak, Kishore Mosaliganti, Sean Megason)
# [[JPEG2000 and HDF5 Image Readers in ITK]] (Kishore Mosaliganti, Luis Ibanez, Sean Megason)
# [[MedianTexture|Median binary pattern texture measures for cell nuclei segmentation]] (Adel Hafiane, Lucas Menand, K. Palaniappan, Sean Megason)
# [[StandardsInterfaces|Standards and Interfaces for Microscopy Image Analysis in ITK]] (whoever has an opinion, Sean Megason)

=== Shape Analysis ===
#[[2010_Summer_Project_Week_Shape|Median Shape by Boundary-based Distance ]](Tammy Riklin Raviv, Sylvain Bouix)
# [[Shape Analysis projects, integration with Slicer3]] (Beatriz Paniagua, Martin Styner)
# [[Particle Based Shape Regression]] (Manasi Datar, Joshua Cates, P. Thomas Fletcher, Sylvain Gouttard, Guido Gerig, Ross Whitaker)
#[[Automatic SPHARM Shape Analysis in 3D Slicer ]] (Corentin Hamel, Clement Vachet, Beatriz Paniagua, Nicolas Augier, Martin Styner)
#[[2010_Summer_Project_Week_Shape_Test_Bench|Shape Test Bench]] (Marc Niethammer, Sylvain Bouix)

=== Diffusion ===
#[[2010_Summer_Project_Week_Diffusion|Fluid Mechanics Based Tractography ]](Nathan Hageman)
#[[Efficient Diffusion Connectivity via Multidirectional Fstar]] (Alexis Boucharin, Clement Vachet, Yundi Shi, Mar Sanchez, Martin Styner)
#[[2010_Summer_Project_Two_Tensor|Implementing Two-tensor tractography in Slicer (Python) ]](Stefan Leinhard, James Malcolm, Demian Wasserman, Yogesh Rathi)
#[[Application of the DTI pipeline to the teenage substance abuse study]] (Gopalkrishna Veni, Sarang Joshi, Ross Whitaker)
#[[NAMIC Tools Suite for DTI analysis]] (Hans Johnson, Joy Matsui, Vincent Magnotta, Sylvain Gouttard)
#[[2010_Summer_Project_QSpace_Reconstruction_for_Diffusion_Spectrum_Imaging_Data|QSpace Imaging Reconstruction for Diffusion Spectrum Imaging Data]] (Sudhir Pathak)

=== NA-MIC Kit Internals ===
#Module Inventory (Steve, Jim)
#[[2010 NAMIC Project week: Viewer Manager Factory|Viewer Manager Factory]] (Alex Yarmarkovich, Kilian, Steve, Nicole)
#[[2010 NAMIC Project week: Programmatic use of Volume Rendering module|Programmatic use of Volume Rendering module]] (Andrey Fedorov, Yanling Liu, Alex Yarmarkovich)
#[[2010_NAMIC_Project_week:Slicer4Icons|Consistent visual language for Slicer4: icon rework marathon]] (Wendy Plesniak)
#[[2010_NAMIC_Project_week:LongitudinalPETSUV_Wizard | Slicer Wizard for PET/CT workflow]] (Wendy Plesniak, Ron Kikinis)
#[[2010_Summer_Project_Week_PythonQt|PythonQt and console widget]] (Steve Pieper, Jean-Christophe Fillion-Robin)
#[[2010_Summer_Project_Week_VTKWidgets|VTKWidgets]] (Jean-Christophe Fillion-Robin, Will Schroeder, Nicole Aucoin, Wendy, Ron Kikinis)
#[[2010_Summer_Project_Week_Superbuild |Superbuild ]]Superbuild (David Partyka, Steve Pieper, Katie Hayes)
#[[Paraview Support for Computational Anatomy]] (Michel Audette, Mike Bowers)

== Preparation ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# The NA-MIC engineering team will be discussing infrastructure projects in a kickoff TCON on April 15, 3pm ET. In the weeks following, new and old participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!
# By 3pm ET on June 10, 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.
# By 3pm on June 17, 2010: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
## 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)
## Gather test images in any of the Data sharing resources we have (e.g. XNAT/MIDAS). 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.)
## 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)
# 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...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-6/#dirlist Slicer-3-6 branch] (new code should not be checked into the branch).
## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].

==Attendee List==

<big>'''NOTE:'''</big> <font color="maroon">THIS IS AN AUTOMATICALLY GENERATED LIST FROM THE REGISTRATION WEBSITE. ATTENDEES SHOULD '''NOT''' EDIT THIS, BUT [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 REGISTER BY CLICKING HERE.]</font>

# Anderson Peter , GE Navigation (Retired)
# Aucoin Nicole , BWH
# Audette Michel , Kitware
# Aylward Stephen , Kitware, Inc
# Boucharin Alexis , UNC Neuro Image Research and Analysis Laboratories
# Bouix Sylvain , BWH
# Bowers Michael , Johns Hopkins University
# Budin Francois , UNC
# Burdette Everette , Acoustic MedSystems, Inc.
# CHAUVIN Laurent , Brigham and Women's Hospital
# Chen Min , Johns Hopkins University
# Crane Jason , UCSF
# Datar Manasi , SCI Institute
# Ding Liya , The Ohio State University
# Eckbo Ryan , BWH
# Ersoy Ilker , University of Missouri Columbia
# Fedorov Andriy , Surgical Planning Lab
# Fillion-Robin Jean-Christophe , Kitware Inc.
# Finet Julien , Kitware Inc
# Fishbaugh James , SCI Institute
# Fritscher Karl , UMIT
# Gao Yi , Gerogia Tech
# GELAS Arnaud , Harvard Medical School
# Gorgolewski Chris , SPL
# gouaillard alexandre , CoSMo Software
# Gouttard Sylvain , SCI Institute
# Grama Kedar , Rensselaer Polytechnic Institute
# Haehn Daniel , University of Pennsylvania
# Hafiane Adel , ENSI-Bourges
# Hageman Nathan , UCLA
# Hahn Dieter , University Erlangen
# Halle Michael , BWH/SPL
# Hamel Corentin , UNC Chapel Hill
# Hata Nobuhiko , Brigham and Women's Hospital
# Hayes Kathryn , Brigham and Women's Hospital
# Herlambang Nicholas , AZE, Ltd.
# Holton Leslie , Medtronic Navigation
# Ibanez Luis , KITWARE Inc.
# Jagadeesan Jayender , SPL
# Johnson Hans , University of Iowa
# Kapur Tina , Brigham and Women's Hospital
# Kikinis Ron , Brigham and Women's Hospital
# Kim Minjeong , UNC-Chapel Hill
# Kolesov Ivan , Georgia Institute of Technology
# Larson Garrett , UNC-CH
# Lee Joohwi , UNC Chapel Hill
# Li Rui , MGH
# Lienhard Stefan , LMI
# Lisle Curtis , KnowledgeVis, LLC
# Liu Felix , Beth Israel Deaconess Medical Center
# Liu Yanling , SAIC-Frederick, Inc.
# Liu Haiying , Brigham and Women's Hospital
# Lowekamp Bradley , Lockheed Martin
# machiraju raghu , The Ohio State University
# Magnotta Vincent , The University of Iowa
# malaterre mathieu , CoSMo Software
# Marcus Daniel , Washington University
# Marks William , Focused Ultrasound Lab, BWH, HMS
# Mastrogiacomo Katie , Brigham and Women's Hospital
# Matsui Joy , University of Iowa
# Megason Sean , Harvard Medical School
# Meier Dominik , BWH, Boston MA
# menze bjoern , CSAIL MIT
# menze bjoern , CSAIL MIT
# Milchenko Mikhail , WUSTL
# Miller James , GE Research
# Mosaliganti Kishore , Harvard Medical School
# Niethammer Marc , UNC Chapel Hill
# Norton Isaiah , BWH Neurosurgery
# Olson Beck , UCSF
# Onofrey John , Yale University
# Padmanabhan Raghav , RPI
# Palaniappan Kannappan , university of Missouri
# Paniagua Beatriz , University of North Caolina at Chapel Hill
# Papademetris Xenophon , Yale University
# Partyka David , Kitware Inc
# Patel Pratik , Brainlab Inc
# Pathak Sudhir , Univeristy Of Pittsburgh
# PECOT Thierry , Ohio State University
# Peroni Marta , Politecnico di Milano, MIT, MGH
# Perrot-Audet Antonin , Harvard Medical School
# Pieper Steve , Isomics, Inc.
# Plesniak Wendy , BWH
# Pohl Kilian , IBM
# Pujol Sonia , Brigham and Women's Hospital
# Rannou Nicolas , Harvard Medical School
# Riklin Raviv Tammy , MIT, CSAIL
# Ruiz Marco , UCSD
# Schroeder William , Kitware
# Scully Mark , The Mind Research Network
# Sharp Greg , MGH
# Shi Yundi , UNC Chapel Hill
# Shusharina Nadya , MGH
# Singh Shantanu , The Ohio State University
# Smith Gareth , Wolfson Medical Imaging Centre (WMIC)
# Souhait Lydie , Harvard Medical School
# Spinczyk Dominik , Silesian University of Technology
# Srinivasan Padmapriya , BWH
# Tao Xiaodong , GE Research
# Tokuda Junichi , Brigham and Women's Hospital
# Ungi Tamas , Queen's University
# Vachet Clement , UNC Chapel Hill
# Veni Gopalkrishna , SCI Institute
# Wassermann Demian , SPL/LMI/PNL
# Weinrich Adam , Nokia
# Wells Sandy , BWH
# White Phillip , BWH/HMS
# Wu Guorong , University of North Carolina at Chapel Hill
# Yamada Atsushi , Nagoya Institute of Technology
# Yarmarkovich Alexander , ISOMICS
# Zaitsev Alexander , Brigham and Womens Hospital

File:Caudate orig.png

2010-06-16T14:23:00Z

Sylvain:

2009-06-11T19:05:51Z

Sylvain:

2008-01-04T16:53:58Z

Sylvain: /* List of Participants */

__NOTOC__

Back to [[AHM_2008]], [[Events]]

=Tour of the Scientific Computing and Imaging (SCI) Institute=

The SCI Institute (http://www.sci.utah.edu) recently moved into the new Warnock Engineering Building at the University of Utah.

Here is information about our new space:

http://www.sci.utah.edu/stories/2007/Warnock.html

http://www.sci.utah.edu/stories/2007/sci_moving.html

Please join us for a short tour of the SCI Institute.

=Logistics=
We have rented a van/bus that will hold 20 people and take people from the Marriott Hotel to the SCI Institute and then back again to the Hotel.

The van/bus will leave the Marriott Hotel at '''5:00 p.m. on Wednesday, January 9''' and return to the Hotel around 6:15 p.m.

The first 20 people to sign up will be able to ride on the van/bus. Others who would like to join us can car pool.

Here is a map and directions to the SCI Institute (Warnock Engineering Building). It is about a 10 minute drive from the Hotel to the SCI Institute.

http://www.sci.utah.edu/map.html

=List of Participants=
Please add your name to the bottom of the list, if you intend to participate in the tour.
#Ron Kikinis
#Zohara Cohen
#Curtis Lisle
#Martin Styner
#Vincent Magnotta
#Nicole Grosland
#Carl-Fredrik Westin
#Nikos Chrisochoides
#Marek Kubicki
#Sonia Pujol
#Csaba Csoma
#Steve Pieper
#Randy Gollub
#Terry Yoo
#Michael Halle
#Luis Ibanez
#Carlo Pierpaoli
#Sylvain Bouix

TractographyWorkshop Core1 ActionPlan

2007-12-17T22:56:01Z

Sylvain: /* Challenges in data processing */

This page refers to an active research project within NA-MIC. If you are interested in participating, we welcome your input. Please contact Randy Gollub

== At the Workshop we agreed to complete the following: ==
1) Define a NA-MIC endorsed DWI pre- and post processing pipeline that uses NA-MIC toolkit software when available and other freely available software if unanimously agreed upon by the group (e.g. some FSL tools are in widespread use, but for academic use only) for this project. Any tools not compatible with NAMIC licensing that are essential to the pipeline will be put on a short list for future NA-MIC development. <br />
2) Curate and post in the NA-MIC Publication database one or more sets of DWI data to be used within NA-MIC for analytic tool development, testing and calibration.<br />
3) Complete a rigorous analysis of the properties of the tractography approaches in use or under development within NA-MIC Core 1 teams on these data sets, including test-retest reliability.<br />
4) Prepare and submit for publication a scholarly report of this work, with Sonia Pujol taking the lead under the mentorship of CF Westin, Ross, Guido and Randy. All participants in the work will share authorship.
<br />

== Brief summary of the presentations, comparative analysis of tractography methods/approaches: ==
There were both common and disparate results across tractography approaches. All methods resulted in a high degree of intersubject variability. That drove the decision to find a dataset that included at least one within subject replication. Even greater detail as to how algorithms differ in reporting results were noted, e.g. weighting number of streamlines per voxel and how that affects the voxelwise statistical calculations (hence the decision below to use volume rather than number of streamlines in next iteration). The same data sets were found to be outliers and/or posed greater challenges in several of the algorithms. Many of the algorithms couldn't use the posted ROIs because they needed volume ROIs not slice plane ROIs. The different laboratories actually used fairly similar methods to generate volume based ROIs so coming to consensus on how to do this for the next round was easy. This was one of the most time consuming steps, so getting it correct on the next round will be a big advantage. The presentations also highlighted the exact details of how different preprocessing steps and choice of ROIs affected the outcome of the different algorithms. This led to a unanimous agreement to chose only one pre-processing pipeline for the next phase of our work (needs to include white matter mask) and greatly simplified the decision-making for the next phase. We were all struck by the vast range of results of the different tractography algorithms even after controlling for many of the preprocessing steps. Notably, not every site completed and/or presented analysis of full cohort/ROI set. We agreed to wait until the next round when improved consistency in data processing and complete results for each algorithm were available before making any cross algorithm comparisons.There was unanimous agreement that this effort is timely for the field of DTI.
<br />

== Methods for Phase 2 NA-MIC DWI tractography analysis ==
1) All participants agreed to continue, so list of algorithms will be the same as presented in Santa Fe with potential addition of others if needed. That will be decided at the January AHM.

2) Agreed to change datasets in favor of a different dataset with more directions and that has two identical sessions (test-retest) so that within subject reliability can be assessed for each algorithm. Candidate dataset under consideration is the 10 MIND subject Reliability data from MGH. Sylvain B (BWH) volunteered to make nrrd headers for the 10 MIND subjects data from MGH test/retest with help from Jeremy, Vince, and Randy as needed. Sylvain B (BWH) has initial dataset and will report back on Friday any problems before preparing the rest. He is posting this initial dataset on the portal (see download instructions) with a corrected nrrd header and the initial preprocessing steps completed. It is ready for all sites to test out. Missing is the distortion correction using the field maps that is still being worked on at MGH/BWH.

3) We will use the same 5 tracts used for the Santa Fe Workshop plus the Corpus Callosum (CC). These ROIs do a good job of spanning the range of tractography challenges (e.g. large to small, various amounts of crossing fibers, various degrees of curvature). The ROIs need to be redone to be a volume rather than a plane. Agreed to use same definitions for locating the centroid of the ROI then expand to make a volume ROI. Sonia and Randy to make first pass in the same initial subject, validate with Marek's lab and then send around to be sure they work with all of the algorithms.

== Downloading the data: ==
1) If you do not already have an account with BIRN/SRB, request one [https://portal.nbirn.net here]. Send [mailto:jbockholt@mrn.org jeremy] an e-mail message so that he can remind BIRN to expedite the account request for NA-MIC project.

2) If/When you have a BIRN/SRB account, send [mailto:jbockholt@mrn.org jeremy] an e-mail message so that he can invite you to the NA-MIC DTI validation project. You will not be able to download the data unless you are invited to the data sharing project for this.

3) Use the SRB SCommands to get the data
<code>Scd /home/Projects/NAMIC_DTI_VALIDITY__0074/Data</code>

==== List of all of the data-sets ====
For each visit of the 10 subjects:
** the tar.gz archive contains the initial raw data,
** the tgz archive contains the pre-processed data (Eddy current and EPI correction).

*'''Contents of the initial raw datasets'''
**dti/
***.dcm contains the raw DTI dicom images
***.nrrd contains the corresponding NRRD headers for the dicom
**t1/
***.dcm contains the raw T1-weighted dicom images
**t2/
***.dcm contains the raw T2-weighted dicom images
**fieldmag/
***.dcm contains the magnitude image of field map
**fieldphase/
***.dcm contains the phase image of field map

*'''Contents of the pre-processed datasets'''
{| border="1"
|+ Example contents of the M02100024_visit1_nhdr.tgz file
|-
! FileName
! Description
|-
| M02100024_visit1/M02100024_visit1_dti-Ed-Epi.nhdr
| Eddy current corrected, EPI corrected and weighted least square estimation diffusion tensor
|-
| M02100024_visit1/M02100024_visit1_dwi-Ed-Epi.nhdr
| Eddy current corrected and EPI corrected dwi
|-
| M02100024_visit1/M02100024_visit1_dti-Ed.nhdr
| Eddy current corrected and weighted least square estimation diffusion tensor
|-
| M02100024_visit1/M02100024_visit1_dwi-Ed.nhdr
| Eddy current corrected dwi
|-
| M02100024_visit1/M02100024_visit1_dwi.nhdr
| Raw dwi
|-
| M02100024_visit1/M02100024_visit1_fieldmag1.nhdr
| Field Map used for EPI correction.
|-
| M02100024_visit1/M02100024_visit1_fieldmag2.nhdr
|
|-
| M02100024_visit1/M02100024_visit1_fieldphase1.nhdr
|
|-
| M02100024_visit1/M02100024_visit1_fieldphase2.nhdr
|
|-
| M02100024_visit1/M02100024_visit1_t1.nhdr
| t1 weighted scan
|-
| M02100024_visit1/M02100024_visit1_t2.nhdr
| t2 weighted scan
|}

You can specifically grab one of data-sets at a time by following the example below
<code>Sget M02100024_visit1.tar.gz</code>
<code>Sget M02100024_visit1_nhdr.gz</code>

* '''List of datasets'''

case M02100023

<code>M02100023_visit1.tar.gz</code><br>
<code>M02100023_visit2.tar.gz</code><br>
n.b., M02100023 does not have EPI corrected as the fieldmap does not work.

case M02100024

'''<code>M02100024_roi.tar.gz</code><br>'''
<code>M02100024_visit1.tar.gz</code><br>
<code>M02100024_visit2.tar.gz</code><br>
'''<code>M02100024_visit1_nhdr.tgz</code><br>'''
'''<code>M02100024_visit2_nhdr.tgz</code><br>'''

case M52200010

'''<code>M52200010_roi.tar.gz</code><br>'''
<code>M52200010_visit1.tar.gz</code><br>
<code>M52200010_visit2.tar.gz</code><br>
'''<code>M52200010_visit2_nhdr.tgz</code><br>'''

case M52200011

'''<code>M52200011_roi.tar.gz</code><br>'''
<code>M52200011_visit1.tar.gz</code><br>
<code>M52200011_visit2.tar.gz</code><br>
'''<code>M52200011_visit1_nhdr.tgz</code><br>'''
'''<code>M52200011_visit2_nhdr.tgz</code><br>'''

case M52200012

'''<code>M52200012_roi.tar.gz</code><br>'''
<code>M52200012_visit1.tar.gz</code><br>
<code>M52200012_visit2.tar.gz</code><br>
'''<code>M52200012_visit1_nhdr.tgz</code><br>'''
'''<code>M52200012_visit2_nhdr.tgz</code><br>'''

case M87101083

'''<code>M87101083_roi.tar.gz</code><br>'''
<code>M87101083_visit1.tar.gz</code><br>
<code>M87101083_visit2.tar.gz</code><br>
'''<code>M87101083_visit1_nhdr.tgz</code><br>'''
'''<code>M87101083_visit2_nhdr.tgz</code><br>'''

case M87101118

'''<code>M87101118_roi.tar.gz</code><br>'''
<code>M87101118_visit1.tar.gz</code><br>
<code>M87101118_visit2.tar.gz</code><br>
'''<code>M87101118_visit1_nhdr.tgz</code><br>'''
'''<code>M87101118_visit2_nhdr.tgz</code><br>'''

case M87102103

'''<code>M87102103_roi.tar.gz</code><br>'''
<code>M87102103_visit1.tar.gz</code><br>
<code>M87102103_visit2.tar.gz</code><br>
'''<code>M87102103_visit1_nhdr.tgz</code><br>'''
'''<code>M87102103_visit2_nhdr.tgz</code><br>'''

case M87102104

<code> M87102104_roi.tar.gz</code><br>'''
<code>M87102104_visit1.tar.gz</code><br>
<code>M87102104_visit2.tar.gz</code><br>
'''<code>M87102104_visit1_nhdr.tgz</code><br>'''
'''<code>M87102104_visit2_nhdr.tgz</code><br>'''

case M87102113

<code> M87102113_roi.tar.gz</code><br>'''
<code>M87102113_visit1.tar.gz</code><br>
<code>M87102113_visit2.tar.gz</code><br>
'''<code>M87102113_visit2_nhdr.tgz</code><br>'''

====Regions of Interest:====

The ROIs are located in /home/Projects/NAMIC_DTI_VALIDITY__0074/Data

** Cingulum, Cingulum Hippocampal, Internal Capsule, Uncinate Fasciculus:
left side: source: label #2 ; sink: label #7

right side: source: label #8; sink: label #9

** Fornix:
left side: source #2; sink #7

right side: source #2; sink #9

** Corpus Callosum Forceps Major, Corpus Callosum Forceps Minor:
source: label #2; sink: label #7

== Preprocessing stream: ==
1) Start with DWI data and NiFTY header + gradient directions (UPLOAD- raw)<br />
2) Field Maps are available and Sylvain has verified that using them to correct the distortions would be desirable. Still to be done is to get help in using them to do the correction. Randy will work with MGH collaborators to get this to work and will update the group on Friday. (UPLOAD) <br />
3) Eddy Current Correction (affine registration) (to be done at BWH by Sylvain/Sonia) (UPLOAD)<br />
4) Put into nrrd format (to be done at BWH by Sylvain/Sonia) <br />
5) Use weighted least squares tensor estimation using TEEM library (to be done at BWH by Sylvain/Sonia) (UPLOAD)<br />
6) T1 white matter mask co-registered to eddy current corrected DWI data (NA-MIC affine registration tool) (Freesurfer white matter + ? vs. EMSegmentation- Sonia/Sylvain to determine based on what works best) (UPLOAD) <br />
7) ROIs will be drawn in DWI/DTI space (to be done at BWH by Sylvain/Sonia) (UPLOAD) <br />
8) Affine registration transformation to bring retest into test space (use this for mapping ROIs and outcome label maps only from test to retest for each subject) (UPLOAD) <br />
9) Each group will be responsible for implementing their own algorithm starting at whatever point in this stream is appropriate for their software. All agreed NOT to use alternate methods to accomplish any of the afore listed steps.<br />
<br />

=== Challenges in data processing ===
Please list below any challenges that you encounter in processing the data.

# Automatic mask generation, threshold detection failed for provided tensors. workaround: Reestimate tensors with fixed threshold.
# Image frame and measurement frame not equivalent for tensor data.
# Several datasets corrupted
## M87102113_visit1 - fixed (Sylvain)
## M02100023_visit1 and visit2 - should not be used in study (Sylvain)
## M52200010_visit1 - fixed (Sylvain)
# ROI origin not the same as tensor data. workaround: set origin to (0,0,0) in ROI images.

== Outcome metrics: ==
This is still under discussion, but for the next round of presentations at the January All Hands meeting we agreed to do the following:<br />
1) Everyone will email Sonia Pujol their slides from the Santa Fe meeting and she and I will use them to compile an Excel worksheet for each laboratory to fill in as they process the new dataset. This will include information such as: <br />
a) Space carved (Casey's [http://www.sci.utah.edu/~gcasey/research/code DTIprocess] tool that generates a volume label map measure from traceline). This will give volumes, overlap, mean and Std Dev of FA, trace, mode. We will use these for test-retest metrics. Each group will pass these label maps to Sonia and she will generate these measures for the AHM presentation. <br />
b) User interface, hardware/software (processor speed, platform, RAM), operator time<br />
c) Parameter settings for each algorithm<br />
<br />
2) We will try to use Casey's FiberCompare multiple traceline visualization tool to compare results<br />
3) Sonia will also use the label map results to explore ways to analyze them, e.g. Staple algorithm to find common agreement (specificity and sensitivity)<br />

Further discussion of how best to parameterize tracts will be a key point for the January AHM<br />

==Uploading your results==
The following directory has been created on the SRB for uploading your results<br />

/home/Projects/NAMIC_DTI_VALIDITY__0074/Results<br />

The below example assumes that you have the SRB Scommands installed and configured for a user that is a member of the NAMIC_DTI_VALIDITY__0074 group<br />

<code>Scd /home/Projects/NAMIC_DTI_VALIDITY__0074/Results</code><br />
<code>Smkdir MyTestResultsDir</code><br />
<code>Schmod a NAMIC_DTI_VALIDITY__0074 groups MyTestResultsDir</code><br />
<code>Scd MyTestResultsDir</code><br />
<code>Sput MyTestFile .</code><br />
<code>Schmod a NAMIC_DTI_VALIDITY__0074 groups MyTestFile</code><br />
<br />

== Next steps:==
1) [http://www.na-mic.org/Wiki/index.php/Tractography_Study_Telephone_Conference_Nov_16_2007 Telephone Conference Call from November 16, 2007 Meeting Notes] Agenda items include feedback on sample data set & ROIs Call in information: 1-800-861-4084, ID 1040119 #<br />
2) Next face to face gathering will be at the AHM, Randy scheduled time on the agenda Wednesday to continue this project <br />
3) Proper implementation of DTI gradient orientation system in ITK, nrrd, TEEM, etc (Casey/Tom to file bug report, bring it up in an upcoming Engineering T-con, plan for work on it next Project week))<br />

== Miscellaneous Notes ==
Group explored potential data sets (UNC n=1, 10 acquisitions with 6 directions; MIND n=10, 8 acquisitions, 2x at each of 3 sites with 6 directions and 2x at 1 site with 60 directions) that are available as needed.
<br />

Return to [[Projects/Diffusion/2007_Project_Week_Contrasting_Tractography_Measures | Contrasting Tractography Project Page]]