Jointly estimating parametric maps of multiple diffusion models from undersampled q-space data: A comparison of three deep learning approaches.
Journal:
Magnetic resonance in medicine
Published Date:
Jan 26, 2022
Abstract
PURPOSE: While advanced diffusion techniques have been found valuable in many studies, their clinical availability has been hampered partly due to their long scan times. Moreover, each diffusion technique can only extract a few relevant microstructural features. Using multiple diffusion methods may help to better understand the brain microstructure, which requires multiple expensive model fittings. In this work, we compare deep learning (DL) approaches to jointly estimate parametric maps of multiple diffusion representations/models from highly undersampled q-space data.