OCMDemo.jl

This is demo code for the paper

Preiswerk, Frank, et al. "Synthesizing dynamic MRI using long-term recurrent convolutional networks", 9th International Conference on Machine Learning in Medical Imaging (MLMI 2018).

How to use

Python 3 with the following packages is required to run the code:

numpy h5py xml keras sklearn matplotlib cv2

Download the code

git clone https://github.com/fpreiswerk/OCM-LRCN

Download the data

To download and extract the data, run the following commands from within the OCM-MRI directory:

wget https://www.dropbox.com/s/y0y2o0q8m9z10fp/OCM-LRCN_example_data.tar.gz
tar -xzf OCM-LRCN_example_data.tar.gz

Training a model on the sample datasets

Run the following python script:

python run_train.py

This will train the model, make predictions and save everything to data/output.

Rendering the results

Run the script

python run_results.py

to generate images and movies of the results. Everything will also be saved to the data/output folder.

Credits

This work was performed with the following co-authors:

• Cheng-Chieh Cheng, Brigham and Women's Hospital, Harvard Medical School, Boston
• Jie Luo, Graduate School of Frontier Sciences, The University of Tokyo, Japan
• Bruno Madore, Brigham and Women's Hospital, Harvard Medical School, Boston