Journal
INTERNATIONAL JOURNAL OF COMPUTER VISION
Volume 129, Issue 3, Pages 656-680Publisher
SPRINGER
DOI: 10.1007/s11263-020-01384-1
Keywords
Diffusion MRI; Brain; Tractography; Machine Learning; Maximum-entropy inference; Algorithm validation
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Funding
- Swiss Federal Institute of Technology Zurich
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A probabilistic model based on the Fisher-von-Mises distribution was proposed for estimating the maximum entropy posteriors of local streamline directions. The optimal precision of posteriors for streamlines was determined using information-theoretic technique to ensure stable results in retest measurements of the same subject.
White matter tractography, based on diffusion-weighted magnetic resonance images, is currently the only available in vivo method to gather information on the structural brain connectivity. The low resolution of diffusion MRI data suggests to employ probabilistic methods for streamline reconstruction, i.e., for fiber crossings. We propose a general probabilistic model for spherical regression based on the Fisher-von-Mises distribution, which efficiently estimates maximum entropy posteriors of local streamline directions with machine learning methods. The optimal precision of posteriors for streamlines is determined by an information-theoretic technique, the expected log-posterior agreement concept. It relies on the requirement that the posterior distributions of streamlines, inferred on retest measurements of the same subject, should yield stable results within the precision determined by the noise level of the data source.
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