Water removal in MR spectroscopic imaging with Casorati singular value decomposition

0580462 - ÚPT 2025 RIV US eng J - Journal Article
Shamaei, Amirmohammad - Starčuková, Jana - Rizzo, R. - Starčuk jr., Zenon
Water removal in MR spectroscopic imaging with Casorati singular value decomposition.
Magnetic Resonance in Medicine. Roč. 91, č. 4 (2024), s. 1694-1706. ISSN 0740-3194. E-ISSN 1522-2594
R&D Projects: GA MŠMT(CZ) EF18_046/0016045; GA MŠMT(CZ) LM2018129; GA MŠMT(CZ) LM2023050
EU Projects: European Commission(XE) 813120 - INSPiRE-MED
Institutional support: RVO:68081731
Keywords : functional MRS * low-rank approximations * MR spectroscopic imaging * water removal * water suppression
Impact factor: 3.3, year: 2022
Method of publishing: Open access
https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29959

Purpose: Water removal is one of the computational bottlenecks in the processing of high-resolution MRSI data. The purpose of this work is to propose an approach to reduce the computing time required for water removal in large MRS data. Methods: In this work, we describe a singular value decomposition–based approach that uses the partial position-time separability and the time-domain linear predictability of MRSI data to reduce the computational time required for water removal. Our approach arranges MRS signals in a Casorati matrix form, applies low-rank approximations utilizing singular value decomposition, removes residual water from the most prominent left-singular vectors, and finally reconstructs the water-free matrix using the processed left-singular vectors. Results: We have demonstrated the effectiveness of our proposed algorithm for water removal using both simulated and in vivo data. The proposed algorithm encompasses a pip-installable tool (https://pypi.org/project/CSVD/), available on GitHub (https://github.com/amirshamaei/CSVD), empowering researchers to use it in future studies. Additionally, to further promote transparency and reproducibility, we provide comprehensive code for result replication. Conclusions: The findings of this study suggest that the proposed method is a promising alternative to existing water removal methods due to its low processing time and good performance in removing water signals.
Permanent Link: https://hdl.handle.net/11104/0349228