CineJENSE - CMRxRecon 2023

This repository corresponds to the STATCOM submission "CineJENSE: Simultaneous Cine MRI Image Reconstruction and Sensitivity Map Estimation using Neural Representations" by Ziad Al-Haj Hemidi, Nora Vogt, Lucile Quillien, Christian Weihsbach, Mattias P. Heinrich, and Julien Oster.

The CineJENSE Model

Results

Dependencies

This code is written in Python 3.7.4 and uses the following packages:

pytorch
numpy
scipy
h5py
tiny-cuda-nn

See pyproject.toml for more details.

Installation

To install the package, run the following command in the terminal:

git clone git@github.com:ziadhemidi/CineJense.git
cd CineJense
poetry lock && poetry install

You also have to install tinycudann. You can do it using the following command:

poetry run pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

You can find more instructions to tincudann here.

Usage

To run the code, you can use the following command:

poetry run python3.9 run_inference.py --input <input_file> --output <output_file> --dataset <dataset_name> --coil <coil_name> --task <task_name>

where <input_file> is the path to the input file, <output_file> is the path to the output file, <dataset_name> is the name of the dataset, <coil_name> is the name of the coil and <task_name> is the name of the task.

For example, to run the code on the dataset TestSet with the coil Multicoil and the task Cine, you can use the following command:

poetry run python3.9 run_inference.py --input /input --output /output --dataset 'TestSet' --coil 'Multicoil' --task 'Cine

License and Citation

This code is licensed under the MIT license. If you use this code, please cite the following paper:

@inproceedings{al2023cinejense,
  title={CineJENSE: Simultaneous Cine MRI Image Reconstruction and Sensitivity Map Estimation Using Neural Representations},
  author={Al-Haj Hemidi, Ziad and Vogt, Nora and Quillien, Lucile and Weihsbach, Christian and Heinrich, Mattias P and Oster, Julien},
  booktitle={International Workshop on Statistical Atlases and Computational Models of the Heart},
  pages={467--478},
  year={2023},
  organization={Springer}
}