Microstructural neuroimaging using spherical convolutional neural networks. (arXiv:2211.09887v1 [eess.IV])

Diffusion-weighted magnetic resonance imaging is sensitive to the
microstructural properties of brain tissue. However, estimating clinically and
scientifically relevant microstructural properties from the measured signals
remains a highly challenging inverse problem. This paper presents a novel
framework for estimating microstructural parameters using recently developed
orientationally invariant spherical convolutional neural networks and
efficiently simulated training data with a known ground truth. The network was
trained to predict the ground-truth parameter values from simulated noisy data
and applied to imaging data acquired …

arxiv convolutional neural networks networks neural networks neuroimaging