DBP2:HarvardFinal:2010 - Revision history

Kubicki: /* Short overview */

2010-11-12T18:04:03Z

Short overview

Kubicki: /* Short overview */

2010-11-12T18:03:43Z

Short overview

Kubicki: /* Short overview */

2010-11-12T18:01:24Z

Short overview

Kubicki: /* Listing and short description of the sample data */

2010-11-12T17:25:56Z

Listing and short description of the sample data

Kubicki: /* Listing and short description of the sample data */

2010-11-12T17:25:44Z

Listing and short description of the sample data

Kubicki: /* Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish */

2010-11-12T17:25:02Z

Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish

Kubicki: /* Short overview */

2010-11-12T17:10:03Z

Short overview

Kubicki: /* Short overview */

2010-11-12T00:42:29Z

Short overview

Kubicki: /* Listing and short description of the sample data */

2010-10-30T20:56:53Z

Listing and short description of the sample data

Kubicki: /* Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish */

2010-10-30T20:49:36Z

Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish

@@ Line 4: / Line 4: @@
 The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography.
 Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.
-[[Image:Slide08.png|thumb|right|200px|]]
+[[Image:Slide08.png|thumb|right|400px|]]
 = Related publications =

@@ Line 4: / Line 4: @@
 The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography.
 Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.
-[[File:[[File:Slide08.png]]]]
+[[Image:Slide08.png|thumb|right|200px|]]
 = Related publications =

@@ Line 4: / Line 4: @@
 The main goal of this project is to develop end-to-end application that would be used to characterize anatomical connectivity abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. During the duration of this project, we developed, disseminated and applied to clinical studies software containing method based on stochastic tractography.
 Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.
 = Related publications =

@@ Line 39: / Line 39: @@
 Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.
 Sample data set can be found here:
 Dataset ([[Media:Stochastic_tutorial_data_TutorialContestSummer2010.zip|ZIP - 92MB]])

← Older revision		Revision as of 17:25, 12 November 2010
Line 38:		Line 38:
	Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.		Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.
	Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.		Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.
		+	Sample data set can be found here:
		+	Dataset ([[Media:Stochastic_tutorial_data_TutorialContestSummer2010.zip\|ZIP - 92MB]])

@@ Line 29: / Line 29: @@
 = Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =
-Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.
+Stochastic Tractography module is currently part of Slicer 3.6 release. The software, when provided two "seeding" regions, finds the probability of connection between those regions, by calculating streamline tracts with a small amount of variation introduced at each step. These tracts are then averaged, to produce a "stochastic" cloud. The module is currently python based. Module is flexible, containing options for data smoothing, filtering, thresholding, changing tractography settings (such as total number of tracts, maximum length, step size, spacing, stopping), as well as types of output (connectivity map, FA, Mode, Trace). Inputs and outputs are compatible with other slicer modules, and can be visualized in Slicer (including 3D volume rendering). End to end tutorial for running stochastic tractography can be found here:
 = Listing and short description of the sample data =
 Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes  contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.
 Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.

@@ Line 2: / Line 2: @@
 = Short overview =
-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.
+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.
 = Related publications =

← Older revision		Revision as of 20:56, 30 October 2010
Line 31:		Line 31:

	= Listing and short description of the sample data =		= Listing and short description of the sample data =
		+	Structural MRI (sMRI). For the Structural MRI volume measures, images were acquired using a 3T GE scanner at BWH in Boston, MA. We used an 8 Channel coil and ASSET with a SENSE-factor of 2. The structural MRI acquisition protocol includes contiguous spoiled gradient-recalled acquisition (fastSPGR) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm.
		+	Diffusion Tensor Imaging (DTI). We used an echo planar imaging (EPI) DTI Tensor sequence. We used a double echo option to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce impact of EPI spatial distortion, we used an 8 Channel coil and ASSET with a SENSE-factor of 2. We acquired 51 directions with b=900, 8 baseline scans with b=0. Scan parameters are: TR 17000 ms, TE 78 ms, FOV 24 cm, 144x144 encoding steps, 1.7 mm slice thickness. 85 axial slices parallel to the AC-PC line cover the whole brain.

← Older revision		Revision as of 20:49, 30 October 2010
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	= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =		= Listing and short description of the software that will run in Slicer 3.6.1 and what it will accomplish =
		+
		+	Stochastic Tractography has been developed to quantify the uncertainty associated with estimated fiber tracts (Bjornemo et al., 2002). Method uses a propagation model based on stochastics and regularization, which allows paths originating at one point to branch and return a probability distribution of possible paths. The method utilizes principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). Based on probability functions, using a sequential importance sampling technique (Bjornemo et al., 2002), one can generate thousands of fibers starting in the same point by sequentially drawing random step directions. This gives a very rich model of the fiber distribution, as contrasted with single fibers produced by conventional tractography methods. Moreover, from a large number of sampled paths, probability maps can be generated, providing better estimates of connectivity between several anatomical locations.
		+
	= Listing and short description of the sample data =		= Listing and short description of the sample data =