Difference between revisions of "2010 Winter Project Week Tractography"

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<h3>Objective</h3>
 
<h3>Objective</h3>
Integrate recent methods for filtered tractography into Slicer3 using Python</div>
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Integrate recent methods for filtered tractography into Slicer3 using Python.</div>
  
 
<div style="width: 27%; float: left; padding-right: 3%">
 
<div style="width: 27%; float: left; padding-right: 3%">
 
<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
Implement various local models and filtering techniques.  Support both region-of-interest and fiducial seeding.</div>
+
Implement various local models and filtering techniques.  Support both region-of-interest and fiducial seeding.  Support both interactive and batch processing.</div>
  
 
<div style="width: 27%; float: left; padding-right: 3%">
 
<div style="width: 27%; float: left; padding-right: 3%">
 
<h3>Progress</h3>
 
<h3>Progress</h3>
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.).  We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography.
+
We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.).  We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography within Slicer.
 
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Revision as of 01:24, 2 December 2009

Home < 2010 Winter Project Week Tractography

Key Investigators

  • BWH: Peter Savadjiev, James Malcolm, Yogesh Rathi, C-F Westin

Objective

Integrate recent methods for filtered tractography into Slicer3 using Python.

Approach, Plan

Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing.

Progress

We have MATLAB implementations of various local models (single-tensor, two-tensor, Watson functions, weighted mixtures of these, etc.) and various model-based filters (Kalman, unscented Kalman, particle, etc.). We have begun converting these to NumPy/Python as well as the additional infrastructure for performing tractography within Slicer.

  1. Savadjiev, Campbell, Pike, Siddiqi "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006.
  2. Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi, "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
  3. Malcolm, Shenton, Rathi, "Neural Tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.