How can spherical CNNs benefit ML-based diffusion MRI parameter estimation?. (arXiv:2207.00572v2 [eess.IV] UPDATED)

This paper demonstrates spherical convolutional neural networks (S-CNN) offer
distinct advantages over conventional fully-connected networks (FCN) at
estimating scalar parameters of tissue microstructure from diffusion MRI
(dMRI). Such microstructure parameters are valuable for identifying pathology
and quantifying its extent. However, current clinical practice commonly
acquires dMRI data consisting of only 6 diffusion weighted images (DWIs),
limiting the accuracy and precision of estimated microstructure indices.
Machine learning (ML) has been proposed to address this challenge. However,
existing ML-based methods are not …

arxiv cnns diffusion ml