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paquiteau / patch-denoising

Patch based denoising method, targeting fMRI thermal noise removal.
https://paquiteau.github.io/patch-denoising/
MIT License
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======================== Patch Denoising Methods

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.. |COVERAGE| image:: https://img.shields.io/codecov/c/github/paquiteau/patch-denoising :target: https://app.codecov.io/gh/paquiteau/patch-denoising .. |CI| image:: https://github.com/paquiteau/patch-denoising/workflows/CI/badge.svg .. |CD| image:: https://github.com/paquiteau/patch-denoising/workflows/CD/badge.svg .. |LICENSE| image:: https://img.shields.io/github/license/paquiteau/patch-denoising .. |DOC| image:: https://img.shields.io/badge/docs-Sphinx-blue :target: https://paquiteau.github.io/patch-denoising .. |RELEASE| image:: https://img.shields.io/pypi/v/patch-denoise :target: https://pypi.org/project/patch-denoise/ .. |STYLE| image:: https://img.shields.io/badge/style-black-black :target: https://github.com/psf/black .. |LINTER| image:: https://img.shields.io/badge/linter-ruff-inactive :target: https://github.com/charliemarsh/ruff .. |PYVERSION| image:: https://img.shields.io/pypi/pyversions/patch-denoise :target: https://pypi.org/project/patch-denoise/ .. |CITATION| image:: https://img.shields.io/badge/paper-hal--openaccess-green :target: https://hal.science/hal-03895194

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 <https://paquiteau.github.io/patch-denoising>_ for more details):

  1. MP-PCA
  2. Hybrid-PCA
  3. NORDIC
  4. Optimal Thresholding
  5. Raw Singular Value Thresholding

A mathematical description of these methods is available in the documentation.

Installation

.. code::

$ pip install patch-denoise

patch-denoise requires Python>=3.9

Quickstart

After installing you can use the patch-denoise command-line.

.. code::

$ patch-denoise input_file.nii output_file.nii --mask="auto"

See patch-denoise --help for detailed options.

Documentation and Examples

Documentation and examples are available at https://paquiteau.github.io/patch-denoising/

Development version

.. code::

$ 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 <https://hal.science/hal-03895194>_ ::

  @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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