deepdumbo / j-modl

Joint model-based deep learning for parallel imaging.

J-MoDL

Joint model-based deep learning for parallel imaging.

Reference article: J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction by H.K Aggarwal and M. Jacob in IEEE Journal of Selected Topics in Signal Processing, (2020).

arXiv link: https://arxiv.org/abs/1911.02945

How to run the codes.

We have tested the codes in Tensorflow 1.15 but it can run in other versions as well. This git repository includes a single image with coil sensitivities in the file tstdata_jmodl.npz. The testing script tst.py will use this image by default and does not require full data download for the testing purpose.

We have also released a subset of the parallel imaging dataset used in this paper. This data is required in the code trn.py. You can download the full dataset from the below link:

Download Link : https://drive.google.com/file/d/1GLqs2A5YpRN8RdDJgdhrspL3zjlG0Qha/view?usp=sharing

By default, without make any change. You can run the testing code as:

$python tst.py. It should give following output.

Output of Joint learning the sampling pattern and network parameters:

Proposed continuous optimization scheme in the 2D case

Overall J-MoDL architecture

Contact

The code is provided to support reproducible research. If the code is giving syntax error in your particular python configuration or some files are missing then you may open an issue or directly email me at [email protected]