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A Bayes Hilbert Space for Compartment Model Computing in Diffusion MRI

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Abstract

The single diffusion tensor model for mapping the brain white matter microstructure has long been criticized as providing sensitive yet non-specific clinical biomarkers for neurodegenerative diseases because (i) voxels in diffusion images actually contain more than one homogeneous tissue population and (ii) diffusion in a single homogeneous tissue can be non-Gaussian. Analytic models for compartmental diffusion signals have thus naturally emerged but there is surprisingly little for processing such images (estimation, smoothing, registration, atlas-ing, statistical analysis). We propose to embed these signals into a Bayes Hilbert space that we properly define and motivate. This provides a unified framework for compartment diffusion image computing. Experiments show that (i) interpolation in Bayes space features improved robustness to noise compared to the widely used log-Euclidean space for tensors and (ii) it is possible to trace complex key pathways such as the pyramidal tract using basic deterministic tractography thanks to the combined use of Bayes interpolation and multi-compartment diffusion models.
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Dates and versions

inserm-01937992 , version 1 (28-11-2018)

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Aymeric Stamm, Olivier Commowick, Alessandra Menafoglio, Simon K Warfield. A Bayes Hilbert Space for Compartment Model Computing in Diffusion MRI. MICCAI 2018 - International Conference on Medical Image Computing and Computer-Assisted Intervention, Sep 2018, Grenade, Spain. pp.72-80, ⟨10.1007/978-3-030-00931-1_9⟩. ⟨inserm-01937992⟩
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