mridc 0.0.1

Data Consistency for Magnetic Resonance Imaging

Data Consistency for Magnetic Resonance Imaging

---

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 how to train models and run a method for reconstruction.

Check on tools for preprocessing and evaluation tools.

Recommended public datasets to use with this repo:

• fastMRI [5].

Documentation

Read the docs here

License

Citation

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

@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.