Denoising MR spectroscopic imaging data with low-rank approximations.
This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability. Denoising is performed by arranging the measured data in appropriate matrix forms (i.e., Casorati and Hankel) and applying low-rank approximations by singular value decomposition (SVD). The proposed method has been validated using simulated and experimental data, producing encouraging results. Specifically, the method can effectively denoise MRSI data in a wide range of SNR values while preserving spatial-spectral features. The method could prove useful for denoising MRSI data and other spatial-spectral and spatial-temporal imaging data as well.
Lucas Center for MR Spectroscopy and Imaging, Department of Radiology, Stanford University, Palo Alto, CA 94304, USA. hnguyen6@stanford.edu
Hien M
HM
Nguyen
Xi
X
Peng
Minh N
MN
Do
Zhi-Pei
ZP
Liang
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D051379:Mice; D009010:Monte Carlo Method; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio