Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction

This repository provides the code and data described in the paper:

Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction Xiajun Jiang, Sandesh Ghimire, Jwala Dhamala, Zhiyuan Li, Prashnna Kumar Gyawali, Linwei Wang

Published on MICCAI 2020.

Please cite the following if you use the data or the model in your work:

@inproceedings{jiang2020learning,
  title={Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction},
  author={Jiang, Xiajun and Ghimire, Sandesh and Dhamala, Jwala and Li, Zhiyuan and Gyawali, Prashnna Kumar and Wang, Linwei},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={487--496},
  year={2020},
  organization={Springer}
}

Code

Requirements

The key requirements are listed under requirements.txt In addition, the code uses modified versions of and torch_geometric. The modified versions of bayesopt and torch_geometric are included with this repo. Go to each folder and install them in the develop mode.

python setup.py develop

Running

ST_GCNN is composed of two stages:

Stage 1: Training a STGCNN model
Stage 2: Evaluating a STGCNN model on simulation datasets.
Stage 3: Evaluating a STGCNN model on clinical datasets.

main.py provides functionality to either of these stages in isolation or in succession.

Configurations: The configurations are provided in the form of a .json file. Two examples are included in the folder config. Results: For each run, a folder named model_name (from .json file) is created in the experiment directory inside which a copy of the .json file, trained model, training logs, diagrams and other results are saved.

Example scripts:

To run stage 1 with settings listed in params_gvae.json:

python main.py --config params_gvae --stage 1

To run stage 2 (assuming stage 1 is complete) with settings listed in params_gvae.json:

python main.py --config params_gvae --stage 2

Data

The signal and geometry of both the heart and torso should be provided. Please contact us for more details.

Contact

Please don't hesitate to contact me for any questions or comments. My email: xj7056@rit.edu.