Low Rank Denoising Tools for MRSI

Tools for low rank denoising of MRSI Source code for various low-rank denoising approaches for MRSI. This package contains functions to carry out: Global and local spatio-temporal low-rank denoising 4 Global and local LORA 4 Linear-predictability denoising 1, 4 SURE optimised local soft thresholding (SURE-SVT) 2 SURE optimised local hard thresholding (SURE-SVHT) 6 Development hosted on the Wellcome Centre for Integrative Neuroimaging GitLab. Python package available from Conda Forge and PyPi. These tools were developed to accompany the publication Clarke WT, Chiew M. Uncertainty in denoising of MRSI using low-rank methods. Magnetic Resonance in Medicine 2022;87:574–588 doi:10.1002/mrm.29018. Please cite this work if you use the tools. References 1: Cadzow JA. Signal enhancement-a composite property mapping algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 1988;36:49–62 doi:10.1109/29.1488. 2: Candès EJ, Sing-Long CA, Trzasko JD. Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators. IEEE Transactions on Signal Processing 2013;61:4643–4657 doi:10.1109/TSP.2013.2270464. 3: Chen Y, Fan J, Ma C, Yan Y. Inference and uncertainty quantification for noisy matrix completion. PNAS 2019;116:22931–22937 doi:10.1073/pnas.1910053116. 4: Nguyen HM, Peng X, Do MN, Liang Z. Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations. IEEE Transactions on Biomedical Engineering 2013;60:78–89 doi:10.1109/TBME.2012.2223466. 5: Song J, Xia S, Wang J, Patel M, Chen D. Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation. arXiv:2004.10959 [cs, eess] 2021. 6: Ulfarsson MO, Solo V. Selecting the Number of Principal Components with SURE. IEEE Signal Processing Letters 2015;22:239–243 doi:10.1109/LSP.2014.2337276.