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Abstract #2900

Fast High-Resolution Deuterium MR Spectroscopic Imaging Integrating Low-Rank and Generalized Series Modelling

Yudu Li1,2,3, Xin Li4, Rong Guo2,5, Ziyang Xu2,6, Yibo Zhao2, Wen Jin2,6, Hannes M. Wiesner4, Xiao-Hong Zhu4, Zhi-Pei Liang2,6, and Wei Chen4
1Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 4Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 5Siemens Medical Solutions USA, Inc., St. Louis, MO, United States, 6Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Synopsis

Keywords: Deuterium, Deuterium

Motivation: Deuterium MRS imaging (DMRSI) enables noninvasive metabolic imaging but suffers from low sensitivity, thus, poor resolution due to the low gyromagnetic ratio of deuterium and low deuterated metabolite concentrations.

Goal(s): To develop a method for fast, high-resolution DMRSI that improves spatial resolution and SNR.

Approach: We proposed a novel data acquisition scheme with variable-density k-space sampling and an image reconstruction scheme that integrates both spectral and spatial priors using a generalized series (GS)-constrained subspace model.

Results: Simulations, phantom studies, and in vivo experiments showed that the method achieved higher resolution, SNR and reproducibility compared to traditional approaches.

Impact: The proposed method improves reliability and resolution for imaging human brain glucose metabolism. The method would prove useful for study of metabolic reprogramming in both healthy and diseased states, particularly, for mapping brain tumor Warburg effects and assessing intra-tumor heterogeneity.

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Keywords