Metadata-Version: 2.1
Name: mrs-denoising-tools
Version: 0.0.1
Summary: Package for low-rank denoising of magnetic resonance spectroscopic imaging
Home-page: https://git.fmrib.ox.ac.uk/wclarke/low-rank-denoising-tools
Author: Will Clarke
Author-email: william.clarke@ndcn.ox.ac.uk
License: UNKNOWN
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 <sup>[4](#Nguyen)</sup>
        - Global and local LORA <sup>[4](#Nguyen)</sup>
        - Linear-predictability denoising <sup>[1,](#Cadzow)</sup><sup>[ 4](#Nguyen)</sup>
        - SURE optimised local soft thresholding (SURE-SVT) <sup>[2](#Candès)</sup>
        - SURE optimised local hard thresholding (SURE-SVHT) <sup>[6](#Ulfarsson)</sup>
        
        ## 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
        <a name="Cadzow">1</a>: 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.
        
        <a name="Candès">2</a>: 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.
        
        <a name="Chen">3</a>: 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.
        
        <a name="Nguyen">4</a>: 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.
        
        <a name="Song">5</a>: 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.
        
        <a name="Ulfarsson">6</a>: 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.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/markdown
