Abstract #1436

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

Hien Nguyen¹, Xi Peng^2,3, 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.