This repository contains the python codes and data files 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
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
