Application
A Slicer extension for microstructure tractography
Contributors
Yogesh Rathi¹, Stefan Lienhard, Yinpeng Li, Martin Styner, Ipek Oguz, Yundi Shi, Christian Baumgartner, Ryan Eckbo
Contact
yogesh@bwh.harvard.edu
Tracing multiple crossing/kissing fibers while estimating the underlying microstructural parameters is a hard tractography problem. We solve this problem using an unscented Kalman filter based tractography algorithm, where model parameters (and thereby the associated microstructural measures) and fiber orientations (multiple) are estimated simultaneously in a consistent manner as the fiber is being traced from the seeding location. The algorithm not only uses the previous estimate (mean) but also uses the covariance in the model parameters (or Fisher-Information metric) to recursively estimate the model parameters at each location thereby providing a regularization of the tracts. Any model can be used. In this software, we provide the following choices: multiple tensors (1, 2 or 3), plus an optional free-water fraction and multi-fiber NODDI model.
Tracts can be visualized in 3D Slicer (open source software available). Each of the estimated diffusion measures are saved along the tracts that can be visualized in Slicer.
Publications
Malcolm, James G., Martha E. Shenton, and Yogesh Rathi. “Filtered multitensor tractography.” IEEE transactions on medical imaging 29.9 (2010): 1664-1675.
Reddy, Chinthala P., and Yogesh Rathi. “Joint multi-fiber NODDI parameter estimation and tractography using the unscented information filter.” Frontiers in neuroscience 10 (2016): 166.
Baumgartner, Christian, et al. “A unified tractography framework for comparing diffusion models on clinical scans.” Computational Diffusion MRI Workshop of MICCAI, Nice. 2012.
Rathi, Yogesh, et al. “Diffusion propagator estimation from sparse measurements in a tractography framework.” Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part III 16. Springer Berlin Heidelberg, 2013.
Affiliations
1Harvard Medical School, Boston, Massachusetts, US