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Stochastic Tractography Module

Slicer3 Training Compendium. Stochastic Tractography Module. Tri Ngo. Introduction. The stochastic tractography filter extracts nerve fiber bundles from DWI images. Unlike streamline tractography, stochastic tractography uses a probabilistic framework to perform tractography.

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Stochastic Tractography Module

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  1. Slicer3 Training Compendium StochasticTractographyModule Tri Ngo

  2. Introduction • The stochastic tractography filter extracts nerve fiber bundles from DWI images. Unlike streamline tractography, stochastic tractography uses a probabilistic framework to perform tractography. • By incorporating uncertainty due to fiber crossings, imaging noise and resolution, stochastic tractography can robustly extract fiber bundles when streamline tractography cannot. • The tracts generated by the stochastic tractography filter can be used to generate a connectivity probability image, which can be used to study connectivity between different regions of the brain. Ngo T.

  3. Materials and Req.’s This course requires the installation of the Slicer3 software and training dataset accessible at the following locations: • Slicer 3 Software http://www.na-mic.org/Wiki/index.php/Slicer:Slicer3 • Training Dataset (packaged and compressed) http://www.na-mic.org/Wiki/images/0/01/IJdata.tar.gz • Prerequisite Skills • Loading images into Slicer 3 Disclaimer It is the responsibility of the user of 3DSlicer to comply with both the terms of the license and with the applicable laws, regulations and rules. Ngo T.

  4. Data This course is built upon three datasets of a single healthy subject brain: DWI (Nrrd) Whitematter Image (Nrrd) ROI Image(Nrrd) shown overlaid on baseline DWI, tan pixel is seed ROI Ngo T.

  5. ROI Image Regions of Interest (ROI) denoted by an integer label. Optionally, other regions can be specified to filter the tracts An ROI image overlaid on FA image, regions with zero label are transparent. The ROI image is an integer image. Each value defines a unique region. Ngo T.

  6. Learning objective DWI, White Matter, ROI Images Following this tutorial, you’ll be ableto extract nerve fiber bundles and compute a connectivity map using the stochastic tractography module. Stochastic Tractography Module Generate Connectivity Map Connectivity Map Ngo T.

  7. Loading Volumes • Obtain the tutorial data and uncompress it. • Use Slicer3 to load in • namic01-dwi_compressed.nhdr • namic01-ROI-leftcingulum.nhdr • namic01_dwi_compressed_WMMask.nhdr Ngo T.

  8. Open ST Filter Module Open stochastic tractography module through the “Modules” drop down menu. Tractography -> Stochastic -> Stochastic Tractography Filter Ngo T.

  9. ST Filter Parameters • Copy the parameters in the image. • The seed point is 2 because that the label in the ROI image of the region we wish to start the tractography. • The maximum tract length is 400. We don’t want to restrict the length of the tract in this case so we set it to a large number. • The remaining options can be left at their default values. Press apply to start the filter. Hint: To learn more about any option, hover over it with the mouse. Ngo T.

  10. Generate Connectivity Map • Open Generate Connectivity Map module using the “Modules” drop down menu. Tractography -> Stochastic -> Generate Connectivity Map Ngo T.

  11. GCMap Parameters • Copy the parameter settings shown in the image. • Input tracts: Locate the filename of the tracts generated using the stochastic tractography module. • Input Volume: This is the dwi image used to generate the tracts. • Output Volume: Tell Slicer3 to create a new output volume. • Click apply button. Hint: To learn more about any option, hover over it with the mouse. Ngo T.

  12. CMap Visualization • The generated connectivity image is called: “Generate Connectivity Map Volume1” • The connectivity map is a floating point image where every voxel’s value is equal to the probability that that voxel is connected to the seed region via a fiber tract. • You can use the “VolumeRendering” module to more easily visualize the data. Ngo T.

  13. Acknowledgments • We would like to acknowledge the support from the following NIH grants: P41 RR13218, R01 MH074794, and U54 EB005149 (National Alliance for Medical Image Computing, NA-MIC). Ngo T.

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