Package for low-rank denoising of magnetic resonance spectroscopic imaging
Project description
Low Rank Denoising Tools
Tools for low-rank denoising of 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
Command line script
Spatio-temporal
mrsi_denoise st [--mask MASK] [-r RANK | -mp] [-p PATCH PATCH PATCH] [-s STEP] input output noise [noise]
LORA
mrsi_denoise lora ...
LP
mrsi_denoise lp ...
SVT/SVHT
mrsi_denoise svt...
/ mrsi_denoise svht ...
Python library
Denoising functions can be found in the mrs_denoising.denoising module.
Citation
If you use these tools please cite:
Clarke WT and Chiew M. ISMRM 2021
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.
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