Water removal in MR spectroscopic imaging with Casorati Singular Value Decomposition

Removing residual water from the MRSI datasets using the SVD-based algorithms is computationally demanding. We present a novel algorithm to reduce the computing time required for water removal in MRSI data. Our proposed method exploits low-rank structures that exist in MRSI data. It arranges the MRSI data in the Casorati matrix form, applies singular value decomposition, and removes residual water from the most prominent left-singular vectors. We compared our proposed method with the HLSVDPRO method, and we achieved 20x acceleration while improving effectiveness. Our proposed method is publicly available as a pip-installable Python tool.