Multi-Fiber Reconstruction from DW-MRI Using a Continuous Mixture of Von Mises-Fisher Distributions
Abstract
In this paper we propose a method for reconstructing the Diffusion Weighted Magnetic Resonance (DW-MR) signal at each lattice point using a novel continuous mixture of von Mises-Fisher distribution functions. Unlike most existing methods, neither does this model assume a fixed functional form for the MR signal attenuation (e.g. 2nd or 4th order tensor) nor does it arbitrarily fix important mixture parameters like the number of components. We show that this continuous mixture has a closed form expression and leads to a linear system which can be easily solved. Through extensive experimentation with synthetic data we show that this technique outperforms various other state-of-the-art techniques in resolving fiber crossings. Finally, we demonstrate the effectiveness of this method using real DW-MRI data from rat brain and optic chiasm.
Cite
Text
Kumar et al. "Multi-Fiber Reconstruction from DW-MRI Using a Continuous Mixture of Von Mises-Fisher Distributions." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008. doi:10.1109/CVPRW.2008.4562991Markdown
[Kumar et al. "Multi-Fiber Reconstruction from DW-MRI Using a Continuous Mixture of Von Mises-Fisher Distributions." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008.](https://mlanthology.org/cvprw/2008/kumar2008cvprw-multifiber/) doi:10.1109/CVPRW.2008.4562991BibTeX
@inproceedings{kumar2008cvprw-multifiber,
title = {{Multi-Fiber Reconstruction from DW-MRI Using a Continuous Mixture of Von Mises-Fisher Distributions}},
author = {Kumar, Ritwik and Barmpoutis, Angelos and Vemuri, Baba C. and Carney, Paul R. and Mareci, Thomas H.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2008},
pages = {1-8},
doi = {10.1109/CVPRW.2008.4562991},
url = {https://mlanthology.org/cvprw/2008/kumar2008cvprw-multifiber/}
}