SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS

This is the official code for "SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS".

The related paper is published at Magnetic Resonance in Medicine.

The baseline code is based on fastMRI code, which is forked from here.

Installation

For dependencies and installation, please follow below:

conda env create -f environment.yml
conda activate ssl_qalas
pip install -e .

Model Training

To train the model, run train_qalas.py as below:

python train_qalas.py --data_path matlab/h5_data --check_val_every_n_epoch 4

Note: some of the variables (e.g., turbo factor or echo spacing) might need to be updated in the fastmri/models/qalas_map.py (L287-L288) based on your sequence.

Training and Validation Logs

To track the training and validation logs, run the tensorboard as below:

tensorboard --logdir=qalas_log/lightning_logs

Inference

To infer the model, run inference_qalas_map.py as below:

python inference_qalas_map.py --data_path matlab/h5_data/multicoil_val --state_dict_file qalas_log/checkpoints/epoch=XXX-step=XXXX.ckpt --output_path matlab/h5_data

The reconstructed maps under matlab/h5_data/reconstructions can be read on Matlab using h5read matlab function:

T1 = h5read('train_data.h5','/reconstruction_t1');
T2 = h5read('train_data.h5','/reconstruction_t2');
PD = h5read('train_data.h5','/reconstruction_pd');
IE = h5read('train_data.h5','/reconstruction_ie');

Generating Training and Validation Data

To make .h5 file, run ssl_qalas_save_h5_from_dicom.m matlab file

If the same subject data is used for validation (i.e., subject specific training and validation), copy train_data.h5 and paste under matlab/h5_data/multicoil_val.

(Optional) To compare the SSL-QALAS with the reference maps (e.g., dictionary matching results), please put them under matlab/map_data (format: .mat file which may contain T1_map, T2_map, PD_map, IE_map, and B1_map)

Sample data can be found here

Cite

If you have any questions/comments/suggestions, please contact at yjun@mgh.harvard.edu

If you use the SSL-QALAS code in your project, please cite the following paper:

@article{jun2023SSL-QALAS,
  title={{SSL-QALAS}: Self-Supervised Learning for rapid multiparameter estimation in quantitative {MRI} using {3D-QALAS}},
  author={Jun, Yohan and Cho, Jaejin and Wang, Xiaoqing and Gee, Michael and Grant, P. Ellen and Bilgic, Berkin and Gagoski, Borjan},
  journal={Magnetic resonance in medicine},
  volume={90},
  number={5},
  pages={2019--2032},
  year={2023},
  publisher={Wiley Online Library}
}