Metadata-Version: 2.1
Name: mridc
Version: 0.0.1
Summary: Data Consistency for Magnetic Resonance Imaging
Home-page: https://github.com/wdika/mridc
Author: Dimitrios Karkalousos
Author-email: d.karkalousos@amsterdamumc.nl
License: Apache-2.0 License 
Platform: UNKNOWN
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

# Data Consistency for Magnetic Resonance Imaging

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---

**Data Consistency (DC) is crucial for generalization in multi-modal MRI data and robustness in detecting pathology.**

This repo implements the following reconstruction methods:

- Cascades of Independently Recurrent Inference Machines (CIRIM) [1],
- Independently Recurrent Inference Machines (IRIM) [2, 3],
- End-to-End Variational Network (E2EVN), [4, 5]
- the UNet [5, 6],
- Compressed Sensing (CS) [7], and
- zero-filled reconstruction (ZF).

The CIRIM, the RIM, and the E2EVN target unrolled optimization by gradient descent. Thus, DC is implicitly enforced.
Through cascades DC can be explicitly enforced by a designed term [1, 4].

## Usage

Check on [scripts](scripts) how to train models and run a method for reconstruction.

Check on [tools](tools) for preprocessing and evaluation tools.

Recommended public datasets to use with this repo:

- [fastMRI](https://fastmri.org/) [5].

## Documentation

[![Documentation Status](https://readthedocs.org/projects/mridc/badge/?version=latest)](https://mridc.readthedocs.io/en/latest/?badge=latest)

Read the docs [here](https://mridc.readthedocs.io/en/latest/index.html)

## License

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

## Citation

Check CITATION.cff file or cite using the widget. Alternatively cite as

```BibTeX
@misc{mridc,
  author={Karkalousos, Dimitrios and Caan, Matthan},
  title={MRIDC: Data Consistency for Magnetic Resonance Imaging},
  year={2021},
  url = {https://github.com/wdika/mridc},
}
```

## Bibliography

[1] CIRIM

[2] Lønning, K. et al. (2019) ‘Recurrent inference machines for reconstructing heterogeneous MRI data’, Medical Image
Analysis, 53, pp. 64–78. doi: 10.1016/j.media.2019.01.005.

[3] Karkalousos, D. et al. (2020) ‘Reconstructing unseen modalities and pathology with an efficient Recurrent Inference
Machine’, pp. 1–31. Available at: http://arxiv.org/abs/2012.07819.

[4] Sriram, A. et al. (2020) ‘End-to-End Variational Networks for Accelerated MRI Reconstruction’, Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
12262 LNCS, pp. 64–73. doi: 10.1007/978-3-030-59713-9_7.

[5] Zbontar, J. et al. (2018) ‘fastMRI: An Open Dataset and Benchmarks for Accelerated MRI’, arXiv, pp. 1–35. Available
at: http://arxiv.org/abs/1811.08839.

[6] Ronneberger, O., Fischer, P. and Brox, T. (2015) ‘U-Net: Convolutional Networks for Biomedical Image Segmentation’,
in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image
Computing and Computer-Assisted Intervention, pp. 234–241. doi: 10.1007/978-3-319-24574-4_28.

[7] Lustig, M. et al. (2008) ‘Compressed Sensing MRI’, IEEE Signal Processing Magazine, 25(2), pp. 72–82. doi:
10.1109/MSP.2007.914728.


