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

Yudu Li, Yibo Zhao, Rong Guo, Tao Wang, Yi Zhang, Matthew Chrostek, Walter C. Low, Xiao Hong Zhu, Zhi Pei Liang, Wei Chen

Research output: Contribution to journalArticlepeer-review


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 data. The learned subspace and manifold structures were integrated via a regularization formulation to remove measurement noise. Theoretical analysis, computer simulations, and in vivo experiments have been conducted to demonstrate the denoising efficacy of the proposed method which enabled high-resolution imaging capability. The translational potential was demonstrated in tumor-bearing rats, where the Warburg effect associated with cancer metabolism and tumor heterogeneity were successfully captured. The new method may not only provide an effective tool to enhance the sensitivity of DMRSI for basic research and clinical applications but also provide a framework for denoising other spatiospectral data.

Original languageEnglish (US)
Pages (from-to)3879-3890
Number of pages12
JournalIEEE transactions on medical imaging
Issue number12
StatePublished - Dec 1 2021


  • In vivo deuterium MRS imaging (DMRSI)
  • high spatiotemporal resolution
  • machine learning
  • subspace modeling

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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