Machine Learning-Enabled High-Resolution Dynamic Deuterium MR Spectroscopic Imaging.

ABSTRACT: Deuterium magnetic resonance spectroscopic imaging (DMRSI) has recently been recognized as a potentially powerful tool for noninvasive imaging of brain energy metabolism and tumor. However, the low sensitivity of DMRSI has significantly limited its utility for both research and clinical applications. This work presents a novel machine learning-based method to address this limitation. The proposed method synergistically integrates physics-based subspace modeling and data-driven deep learning for effective denoising, making high-resolution dynamic DMRSI possible. Specifically, a novel subspace model was used to represent the dynamic DMRSI signals; deep neural networks were trained to capture the low-dimensional manifolds of the spectral and temporal distributions of practical dynamic DMRSI d

SUBMITTER: Li Y 

PROVIDER: S-EPMC8675063 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature