Robust Low-rank Denoising of MRSI Data
Wen Jin1,2, Yibo Zhao1,2, Rong Guo1,2, Yudu Li1,2, Jie Luo3, Yao Li3, and Zhi-Pei Liang1,2
1Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
With the increasing use of MRSI in research and clinical applications, we have seen a surge in the use of low-rank models for denoising MRSI data. This paper presents a novel method for robust denoising of MRSI data corrupted by both Gaussian noise and nuisance signals. The proposed method has been validated using both simulated and in vivo data, producing impressive results. This method will further enhance the practical utility of low-rank denoising.
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