Water removal in MR spectroscopic imaging with Casorati singular value decomposition

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.

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.

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.

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.