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> | Integrate recent methods for filtered tractography into Slicer3 using Python</div> | ||
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<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.</div> | ||
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<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. | ||
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# Savadjiev, Campbell, Pike, Siddiqi "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006. | # Savadjiev, Campbell, Pike, Siddiqi "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006. | ||
# Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi, "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010. | # Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi, "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010. | ||
# Malcolm, Shenton, Rathi, "Neural Tractography using an unscented Kalman filter", IPMI, p.126-138, 2009. | # Malcolm, Shenton, Rathi, "Neural Tractography using an unscented Kalman filter", IPMI, p.126-138, 2009. | ||
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Revision as of 01:21, 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 PythonApproach, Plan
Implement various local models and filtering techniques. Support both region-of-interest and fiducial seeding.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.
- Savadjiev, Campbell, Pike, Siddiqi "3D Curve Inference for Diffusion MRI Regularization and Fibre Tractography", MedIA 10(5), p.799-813, 2006.
- Malcolm, Michailovich, Bouix, Westin, Shenton, Rathi, "A filtered approach to neural tractography using the Watson directional function", MedIA 14(1), p.58-69, 2010.
- Malcolm, Shenton, Rathi, "Neural Tractography using an unscented Kalman filter", IPMI, p.126-138, 2009.
