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Abstract #1436

Spatiotemporal Denoising of MR Spectroscopic Imaging Data by Low-Rank Approximations

Hien Nguyen1, Xi Peng2,3, Minh Do4, Zhi-Pei Liang4

1Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States; 2School of Electronic Information, Wuhan University, China, People's Republic of; 3Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, United States; 4Department of Electrical & Computer Engineering, University of Illinoist at Urbana-Champaign, United States


A new scheme is proposed to denoise magnetic resonance spectroscopic imaging (MRSI) data by exploiting two low-rank structures that exist in MRSI data: one due to partial separability and the other is due to linear predictability. Experimental results demonstrate that the proposed method is effective in denoising MRSI data while preserving spatial-spectral features in a wide range of SNR values.