HQS-Net

pytorch implementation of the paper Learned Half-Quadratic Splitting Network for Magnetic Resonance Image Reconstruction (https://openreview.net/pdf?id=h7rXUbALijU)

Install

python>=3.7.11 is required with all requirements.txt installed including pytorch>=1.10.0

git clone https://github.com/hellopipu/HQS-Net.git
cd HQS-Net
pip install -r requirements.txt

Prepare dataset

you can find more information about OCMR dataset at https://ocmr.info/

## download dataset
wget -nc https://ocmr.s3.amazonaws.com/data/ocmr_cine.tar.gz -P data/
## download dataset attributes csv file
wget -nc https://raw.githubusercontent.com/MRIOSU/OCMR/master/ocmr_data_attributes.csv -P data/
## untar dataset 
tar -xzvf data/ocmr_cine.tar.gz -C data/
## preprocess and split dataset, it takes several hours
python preprocess_ocmr.py

Or you can directly download the preprocessed dataset here, and then put them to data/ folder

Training

Training and testing Scripts for all experiments in the paper can be found in folder run_sh. For example, if you want to train HQS-Net on accleration factor of 5x, you can run:

sh run_sh/acc_5/train/train_hqs_5.sh

or if you want to train Unet based HQS-Net on accleration factors 10x, you can run:

sh run_sh/acc_10/train/train_hqs_unet_10.sh

Testing

For example, if you want to test HQS-Net on accleration factor of 5x, you can run:

sh run_sh/acc_5/test/test_hqs_5.sh

All pretrained models in the paper can be downlowned here, then you should put them to weight/ folder.

We also provide an Colab demo

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Tensorboard

tensorboard for checking the curves while training

tensorboard --logdir log