Yuxin Hu

TL;DR: A new processing framework for DTI is presented that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning.

TL;DR: The goal of this work is to propose a motion robust reconstruction method for diffusion‐weighted MRI that resolves shot‐to‐shot phase mismatches without using phase estimation.

TL;DR: To accelerate and improve multishot diffusion‐weighted MRI reconstruction using deep learning, a parallel version of the Higgs boson– Atorak–Seiden–Bouchut–Stochastic–Molecular–Describing machine learning system is used.

TL;DR: To resolve the motion‐induced phase variations in multi‐shot multi‐direction diffusion‐weighted imaging (DWI) by applying regularization to magnitude images.

TL;DR: The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μM at the group level for the simulated data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.