Difference between revisions of "DBP2:Harvard:Brain Segmentation Roadmap"
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2. Then click "MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY | 2. Then click "MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY | ||
3. Input/Output: | 3. Input/Output: | ||
− | * | + | *Load Volumes |
− | + | 4.Smoothing | |
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*FWHM: | *FWHM: | ||
5. Otsu Mask- tensor estimation | 5. Otsu Mask- tensor estimation | ||
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*This is a thresholding method for the dwi that is based on intensity. | *This is a thresholding method for the dwi that is based on intensity. | ||
6. WM mask | 6. WM mask | ||
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*WM Threshold is based on FA values. Only values within threshold will be included for tractography. | *WM Threshold is based on FA values. Only values within threshold will be included for tractography. | ||
*Artifact Removal- Will add voxels to the white matter mask by evaluating voxels with a high FA that might have been due to an artifact. | *Artifact Removal- Will add voxels to the white matter mask by evaluating voxels with a high FA that might have been due to an artifact. | ||
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*Stopping criteria: Terminates a tract when FA drops below the specified threshold. | *Stopping criteria: Terminates a tract when FA drops below the specified threshold. | ||
8. Probability map | 8. Probability map | ||
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**rough: | **rough: | ||
**cumulative:Tracts are summed by voxel independently | **cumulative:Tracts are summed by voxel independently |
Revision as of 19:11, 17 December 2008
Home < DBP2:Harvard:Brain Segmentation RoadmapBack to NA-MIC Collaborations, Harvard DBP 2
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.
Algorythm
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- C-References
- 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.
- 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
Module
Directions of completing Stochastic Tractography with Python in Slicer 3.0:
1. Loading volumes:
- Go to MODULES > VOLUME.
- Under "Load," click the folder icon and select the volume you want to load. You must load a DWI.nhdr or DWI.nrrd and at least one labelmap for your ROI (in .nhdr or .nrrd). Double-click the file you want in to pop-up window, and then hit apply in Slicer. You have the option to load a second ROI and a white matter mask if you choose to. For the labelmaps, be sure to check the box that says labelmap and then hit "apply"
2. Then click "MODULES > PYTHON MODULES > PYTHON STOCHASTIC TRACTOGRAPHY 3. Input/Output:
- Load Volumes
4.Smoothing
- FWHM:
5. Otsu Mask- tensor estimation
- This is a thresholding method for the dwi that is based on intensity.
6. WM mask
- WM Threshold is based on FA values. Only values within threshold will be included for tractography.
- Artifact Removal- Will add voxels to the white matter mask by evaluating voxels with a high FA that might have been due to an artifact.
7. Tensor Parameters
- Baseline--
- Tensor mode: Is the method for computing the tensors
- FA/Mode/Trace: You can check these if you want the module to create FA/Mode/Trace map.
8. Stochastic Tractography Parameters:
- Total Tracts: The amount of tracts that will be seeded from each voxel
- Maximum tract length (in mm)
- Step Size: length between each re-estimation of tensors.
- Use spacing - -
- Stopping criteria: Terminates a tract when FA drops below the specified threshold.
8. Probability map
- rough:
- cumulative:Tracts are summed by voxel independently
- discriminative: tracts are summed by voxel depending on their length ownership
- ??visual inspection (looking at areas where mask does not exist, but FA exceeds 0.3)
- ??thresholding
- /??visualizing path, tracts
- ??getting numbers (thresholded FA, FA weighted by probability)
- Module documentation can be found here (replace with the new documentation, when its ready):
Work Accomplished
- A - Optimization and testing of stochastic tractography algorythm
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- Original methodological paper, as well as our first attempts to use the algorythm (CC+ and matlab scripts) have been done on old "NAMIC" 1.5T LSDI data (Structural MRI and DTI data).
- Tri worked hard on making sure algorythm works on new high resolution 3T data (available here: 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 algorythm (Figure 4).
Work in Progress
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We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography (Figure 5). 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.
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We use combination of fMRI and DTI data to define and characterize functional and anatomical connectivity within the semantic processing network in schizophrenia.
We have started collaboration with Department of Neurophysiology Max Planck Institute in Frankfurt (Anna Rotarska contact person). They have dataset containing DTI and resting state fMRI in schizophrenia, and want to use stochastic tractography to measure integrity of anatomical connections within the default network in schizophrenia. Their fMRI data has been co-registered with anatomical scans, and we are putting it into the DTI space. Once this is done, we will start creating tracts connecting fMRI ROIs. This data will be also used to test robustness of our module, since data was collected on a different scanner (3T Philips).
We are also working on a tractography comparison projectdataset, where we apply stochastic tractography to phantom, as well as test dataset. Staffing Plan
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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 - Arcuate Fasciculus results.
- 01/2009 - Arcuate Fasciuclus first draft of the 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 Venice, Italy.
- 05/2009 - Presentation of Arcuate Fasciculus findings at World Biological Psychiatry Symposium in Venice, Italy.
Team and Institute
- PI: Marek Kubicki (kubicki at bwh.harvard.edu)
- DBP2 Investigators: Sylvain Bouix, Yogesh Rathi, Julien de Siebenthal
- NA-MIC Engineering Contact: Brad Davis, Kitware
- NA-MIC Algorithms Contact: Polina Gollard, MIT
Publications
In print