This repository contains the python codes for the following paper:
A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data
Hunor Csala, Omid Amili, Roshan D'Souza, Amirhossein Arzani
International Journal for Numerical Methods in Biomedical Engineering: https://doi.org/10.1002/cnm.3858
Python and PyTorch codes are included for the following cases:
- Filling in missing data - synthetic imputation
- itSVD
- softImpute
- PPCA
- Autoencoder
- Denoising noisy data
- Synthetic data denoising
- 4D flow MRI denoising
- Methods:
- RPCA
- Denoising Autoencoder (DAE)
- Noise2Noise (N2N)
- Noise2Void (N2V)
Installation:
The denoising python codes requires the following packages to be installed before running the codes:
-
fancyimpute (for itSVD and softImpute)
https://pypi.org/project/fancyimpute/ -
vtk (for handling vtk input and output data files)
https://vtk.org/download/- Anaconda installation: https://anaconda.org/conda-forge/vtk
-
matplotlib (for visualization only)
https://anaconda.org/conda-forge/matplotlib -
pytorch (for the deep learning methods - DAE, N2N)
https://pytorch.org/get-started/locally/
The N2V the implementation was taken from: https://github.com/juglab/PPN2V
The RPCA implementation was taken from: https://github.com/dganguli/robust-pca
