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Entrack: probabilistic spherical regression with entropy regularization for fiber tractography. (English) Zbl 1483.92090

Summary: 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.

MSC:

92C55 Biomedical imaging and signal processing
68T05 Learning and adaptive systems in artificial intelligence
94A17 Measures of information, entropy

Software:

Adam; Fiberfox
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Full Text: DOI

References:

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