MC²-Net: Motion Correction Network for Multi-Contrast Brain MRI

Tensorflow implementation of "MC²-Net: Motion Correction Network for Multi-Contrast Brain MRI"

Contact: Jongyeon Lee, KAIST (jyl4you@kaist.ac.kr)

Paper

MC²-Net: Motion Correction Network for Multi-Contrast Brain MRI
Jongyeon Lee, Byungjai Kim, and Hyunwook Park
(MRM)

Please cite our paper if you find it useful for your research.

@inproceedings{,
  author = {Jongyeon Lee, Byungjai Kim, HyunWook Park},
  booktitle = {Magnetic Resonance in Medicine},
  title = {MC2-Net: Motion Correction Network for Multi-Contrast Brain MRI},
  year = {2021}
}

Example Results

Quantitative Reuslts

Installation

• Install Tensorflow-gpu 2.2.0 with Python 3.6 and CUDA 10.2

pip install -r requirements.txt

or

conda create -r [ENV_NAME] python=3.6
conda activate [ENV_NAME]
conda install tensorflow-gpu=2.2.0 scikit-image=0.16.2

• Clone this repo

git clone https://github.com/OpenXAIProject/mc2-net.git
cd mc2-net

Dataset

• Dataset used for this study is not the public data. The sample images are in data directory for demo.
• You may use BraTS 2020 as an alternative dataset. To generate the data for training, please refer to the paper and Figure 4 for motion simulation. This step is not implmented in Python code due to its dependency on pulse sequences.

Testing

• Use CUDA_VISIBLE_DEVICES for GPU selection
• Evaluate the predicted images and save them as png files

CUDA_VISIBLE_DEVICES=0 python test.py --load_weight_name ./weight/[weight filename].h5

Training Examples

• Use CUDA_VISIBLE_DEVICES for GPU selection
• Train the sample images

CUDA_VISIBLE_DEVICES=0 python train.py --num_epoch 1000