Improved low-rank filtering of magnetic resonance spectroscopic imaging data corrupted by noise and B₀ field inhomogeneity

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 low-rank filtering, and 2) integrating B₀ field inhomogeneity correction by rank-minimization to make the low-rank model more effective. Results: The proposed method was validated using both simulated and in vivo MRSI data. Its denoising performance is also compared with an upper bound based on the constrained Cramér-Rao lower bound for low-rank filtering. Conclusion : Low-rank filtering can effectively improve the SNR of MRSI data corrupted by both noise and B₀ field inhomogeneity. Significance: The proposed low-rank filtering method will enhance the practical utility of high-resolution MRSI, where SNR has been a limiting factor.