Improved Low-Rank Filtering of Magnetic Resonance Spectroscopic Imaging Data Corrupted by Noise and $B_0$ Field Inhomogeneity

摘要: Goal : To improve the signal-to-noise ratio (SNR) of magnetic resonance spectroscopic imaging (MRSI) data. Methods A low-rank filtering method recently proposed for denoising MRSI data is extended by: 1) incorporating tissue boundary constraints to enable local filtering, and 2) integrating $B_0$ field inhomogeneity correction by rank-minimization make model more effective. Results The was validated using both simulated in vivo Its performance also compared with an upper bound based on constrained Cramer–Rao lower filtering. Conclusion Low-rank can effectively SNR corrupted noise inhomogeneity. Significance will enhance practical utility high-resolution MRSI, where has been a limiting factor.