Documentation/Nightly/Modules/TractographyInteractiveSeeding

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Introduction and Acknowledgements

Title: Tractography Interactive Seeding Author(s)/Contributor(s): Alex Yarmarkovich, Steve Pieper, Demian Wassermann, Isaiah Norton (SPL, LMI, BWH, SlicerDMRI) License: 3D Slicer Contribution and Software License Agreement Acknowledgements: The SlicerDMRI developers gratefully acknowledge funding for this project provided by NIH NCI ITCR U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research), NIH P41EB015898 (National Center for Image-Guided Therapy) and NIH P41EB015902 (Neuroimaging Analysis Center), as well as the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Contact: slicer-users@bwh.harvard.edu Website: http://slicerdmri.github.io/

SlicerDMRI   Surgical Planning Laboratory   NAC   Tractography Fiducial Seeding   Tractography Fiducial Seeding

Module Description

This module is used for interactive seeding of DTI fiber tracts starting from a list of fiducial points or vertices of a model, or a label map volume.

Use Cases

Most frequently used for these scenarios:

• Use Case 1: Create DTI fiber tracts starting from a list of fiducial points. Tracks created from a small number of fiducial points are are highly interactive and can be used in exploration mode while moving fiducials with the help of transformations. This mode can be useful in pre-op planning or for tracking tools during the surgery.
• Use Case 2: Create DTI fiber tracts starting from vertices of a model. This mode can be useful for exploring tracts around a tumor model.
• Use Case 2: Create DTI fiber tracts starting from a label map volume. Tracks created from a label map ROI can be used in interactive mode while modifying the label map using Editor module.

Tutorials

Links to tutorials that use this module:

• Slicer4 Diffusion Tensor Imaging Tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial
• Slicer4 Neurosurgical Planning Tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#Slicer4_Neurosurgical_Planning_Tutorial

Panels and their use

Parameters panel: Parameters: select an existing setting for the module or create a new one. Presets: select an Slicer4 or Slicer3 parameters setting for the module. IO panel: Input DTI volume: select an existing tensor volume. Input FiducialList, Model, or Label Map: select an existing fiducial list, or a model, or a label map volume to be used as seeds for tracking. Output FiberBundle: Select the bundle that you want to create or modify from a list. Enable Seeding Tracts: A toggle to enable/disable seeding. Disable if you want to change multiple parameters without re-running module at each step. Seed Placement Option panel: Fiducial Region Size: The size of the box around each fiducial used for seeding (mm). Fiducial Seeding Step Size: Step between the seeding samples in the box (mm). Seed Selected Fiducials: Flag indicating whether to use only selected fiducials for seeding or all of them. Maximum Number of Seeds: Used with models to specify the limit of the number of vertices to be used for seeding. Tractography Seeding Parameters panel: Minimum Path Length: Select only tracts that are longer than this value (mm). Maximum Path Length: Select only tracts that are shorter than this value (mm). Stopping Criteria: Select Linear Measure or FA to be used as stopping criteria. Stopping Value: Select Linear Measure or FA stopping value, tracking will stop when the value drops below this one. Stopping Track Curvature: Select the curvature stopping value in degree per mm, tracking will stop when the radius of curvature is smaller than this one. Enabling Options panel: Create Tracts Initially As: Select Tubes or Lines Similar Modules Tractography Label Map Seeding UKF Tractography References Interactive Diffusion Tensor Tractography Visualization for Neurosurgical Planning: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3112275/ http://slicerdmri.github.io/ Information for Developers https://github.com/SlicerDMRI