Denoising method for sequence of images or volumes. Primarly targeting fMRI data.
Project description
This repository implements patch-denoising methods, with a particular focus on local-low rank methods.
The target application is functional MRI thermal noise removal, but this methods can be applied to a wide range of image modalities.
It includes several local-low-rank based denoising methods (see the documentation for more details):
MP-PCA
Hybrid-PCA
NORDIC
Optimal Thresholding
Raw Singular Value Thresholding
A mathematical description of theses methods is available in the documentation.
Installation
patch-denoise requires Python>=3.8
Quickstart
After installing you can use the patch-denoise command-line.
$ patch-denoise input_file.nii output_file.nii --mask="auto"
See patch-denoise --help for detailled options.
Documentation and Examples
Documentation and examples are available at https://paquiteau.github.io/patch-denoising/
Development version
$ git clone https://github.com/paquiteau/patch-denoising
$ pip install -e patch-denoising[dev,doc,test,optional]
Citation
If you use this package for academic work, please cite the associated publication, available on HAL
@inproceedings{comby2023, TITLE = {{Denoising of fMRI volumes using local low rank methods}}, AUTHOR = {Pierre-Antoine, Comby and Zaineb, Amor and Alexandre, Vignaud and Philippe, Ciuciu}, URL = {https://hal.science/hal-03895194}, BOOKTITLE = {{ISBI 2023 - International Symposium on Biomedical Imaging 2023}}, ADDRESS = {Carthagena de India, Colombia}, YEAR = {2023}, MONTH = Apr, KEYWORDS = {functional MRI ; patch denoising ; singular value thresholding ; functional MRI patch denoising singular value thresholding}, PDF = {https://hal.science/hal-03895194/file/isbi2023_denoise.pdf}, HAL_ID = {hal-03895194}, HAL_VERSION = {v1}, }
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