Difference between revisions of "2010 Winter Project Week Tractography"

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Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
 
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Image:Filtered_tractography.png|Left hemisphere
 
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==Key Investigators==
 
==Key Investigators==
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* UPenn: Luke Bloy, Ragini Verma
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* BWH: James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin
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* BWH: Carl-Fredrik Westin  
 
  
 
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<h3>Objective</h3>
 
<h3>Objective</h3>
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We would like to provide support for high angular resolution diffusion imaging (HARDI) data models which make use of the symmetric real spherical harmonic functions (RSH) as a basis for functions on the sphere.
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Integrate recent methods for filtered tractography into Slicer3 using Python.</div>
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<h3>Approach, Plan</h3>
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First consensus must be reached on the exact form of the RSH basis to be used. This will provide a basis for future development.  
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<h3>Plan</h3>
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Implement various local models and filtering techniques.  Support both region-of-interest and fiducial seeding. Support both interactive and batch processing. Picking fibers and moving them between polydata structures.</div>
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The functionality we would like to provide is the following:
 
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# MRML representations for images of RSH coefficients
 
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# Visualization of images of RSH coefficients
 
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# Routines for estimating the orientation distribution function (ODF) (Descoteaux2007)
 
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# Routines for estimating the fiber orientation distribution (FOD) (Tournier2007) using both filtered and constrained spherical deconvolution.
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
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We have Python/NumPy 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.) and deterministic tractography infrastructure.
  
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We have implemented the RSH basis and RSH coefficients as ITK based C++ classes, and written ITK image filters to perform the
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However, it is unusably slow (the MATLAB version runs faster). Profiling the code seems to indicate that there is too much NumPy overhead in manipulating lots of small matrices/vectors. Now reimplementing in C/C++.
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model estimation. Slicer modules need to be written to perform the integrated these filters into the Slicer framework.
 
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MRML nodes have been written for RSH volumes. These nodes were based (blindly) off of the DiffusionTensor MRML nodes. There is a mathematics class
 
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which computes scalar maps based off of the RSH coefficients, as well as glpyher which currently only supports sphere sources. Support for line glyph source, to show only the principle diffusion directions would be very beneficial since rendering all the points on the sphere source is very resource intensive.
 
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Visualization is currently being achieved by an extension to the Volumes module in slicer. The display widget nodes to facilitate this were again based on the Diffusion Widget classes.
 
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==References==
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# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi. "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
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*Maxime Descoteaux, Elaine Angelino, Shaun Fitzgibbons, and Rachid Deriche, “Regularized, fast, and robust analytical q-ball imaging,” Magnetic Resonance in Medicine, vol. 58, no. 3, pp. 497–510, 2007.
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# Malcolm, Shenton, Rathi. "Neural tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
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*J-Donald Tournier, Fernando Calamante, and Alan Connelly, “Robust determination of the fibre orientation distribution in diffusion mri: Non-negativity constrained super-resolved spherical deconvolution,” NeuroImage, vol. 35, no. 4, pp. 1459–1472, May 2007.
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# Savadjiev, Zucker, Siddiqi. "On the Differential Geometry of 3D Flow Patterns: Generalized Helicoids and Diffusion MRI Analysis", ICCV 2007
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Latest revision as of 15:46, 8 January 2010

Home < 2010 Winter Project Week Tractography

Key Investigators

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

Objective

Integrate recent methods for filtered tractography into Slicer3 using Python.

Plan

Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding. Support both interactive and batch processing. Picking fibers and moving them between polydata structures.

Progress

We have Python/NumPy 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.) and deterministic tractography infrastructure.

However, it is unusably slow (the MATLAB version runs faster). Profiling the code seems to indicate that there is too much NumPy overhead in manipulating lots of small matrices/vectors. Now reimplementing in C/C++.

  1. Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi. "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
  2. Malcolm, Shenton, Rathi. "Neural tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
  3. Savadjiev, Zucker, Siddiqi. "On the Differential Geometry of 3D Flow Patterns: Generalized Helicoids and Diffusion MRI Analysis", ICCV 2007
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