deepmri's Introduction

DeepMRI

Pytorch implementation of RAKI paper with mild changes and optimizations [1]

Getting Started

Clone the Repo:

git clone https://github.com/geopi1/DeepMRI.git

Datasets

Download the Datasets:

link to mridata

In this site select any of the available MRI scans and download to a folder

An auto download script will be added soon

Prerequisites

Setup conda
```
conda env create -f env.yml
```
This will create a working environment named DeepMRI
Setup can also be performed with pip (virtual env) via the requirements.txt file
```
python3 -m venv DeepMRI
pip install -r requirements.txt
```

Run save_raw_data_to_pickle.py to save the .h5 files from mridata.org as a pickle with np matrices

python save_raw_data_to_pickle.py -p [path_to_wanted_folder]

python save_raw_data_to_pickle.py --data_path [path_to_wanted_folder]

Running Tests

Code

All the hyperparameters of the code are saved as .json in config.json. Please look at the number of epochs.

To run the code, activate the conda environment

conda activate DeepMRI

or select the appropriate python interpreter path and run:

python main.py

a specific path can be added to the command line (instead of the config file)

python main.py -data /path/to/data/folder

Logs

Each run records the training process, saving the learing_rate (lr) and the loss. To view these live via tensorboard:

Navigate to the appropriate folder
Open command line or terminal
from the proper conda env (or it tensorboard is in path) type:

tensorboard --logdir logs_dir/

Results

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

[1] Akçakaya, Mehmet et al. “Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.” Magnetic resonance in medicine vol. 81,1 (2019): 439-453. doi:10.1002/mrm.27420

[2] mridata.org

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