Abstract #3533

Robust Low-rank Denoising of MRSI Data

Wen Jin^1,2, Yibo Zhao^1,2, Rong Guo^1,2, Yudu Li^1,2, Jie Luo³, Yao Li³, and Zhi-Pei Liang^1,2

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

Synopsis

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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