Image quality assessment for fetal MRI

This repo is the implementation of an image quality assessment (IQA) method for fetal MRI, which is the accumulation of the following works:

[1] Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency (MICCAI | arXiv)

[2] Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T (MRM)

[3] A deep learning approach for image quality assessment of fetal brain MRI (ISMRM)

Usage

Train your own models

Brain segmentation (optional)

To use ROI consistency, you would need to generate ROI for your dataset.

1. Download the pre-trained segmentation network
2. Modifty PATH_LABELED_DATA and PATH_UNLABELED_DATA in brainSeg/Code/FetalUnet.py to point to your own dataset.
3. Run:

   cd brainSeg/Code
   python FetalUnet.py
   

Implement your dataset

Implement your dataset following src/mean_teacher/haste.py

Training

cd src
python experiments/haste_exp.py

Use pre-trained model

PyTorch

1. Download pre-trained models (pytorch.ckpt) to torch_iqa_tool/pretrained_models

2. run demo

   cd torch_iqa_tool
   python iqa_demo.py
   

Tensorflow

1. Download pre-trained models (model_ismrm.hdf5 and model_miccai.h5) to tf_iqa_tool/pretrained_models

2. run demo

   cd tf_iqa_tool
   python iqa_demo.py
   

Cite our work

@inproceedings{xu2020semi,
  title={Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency},
  author={Xu, Junshen and Lala, Sayeri and Gagoski, Borjan and Abaci Turk, Esra and Grant, P Ellen and Golland, Polina and Adalsteinsson, Elfar},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={386--395},
  year={2020},
  organization={Springer}
}

@article{gagoski2022automated,
  title={Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T},
  author={Gagoski, Borjan and Xu, Junshen and Wighton, Paul and Tisdall, M Dylan and Frost, Robert and Lo, Wei-Ching and Golland, Polina and van Der Kouwe, Andre and Adalsteinsson, Elfar and Grant, P Ellen},
  journal={Magnetic Resonance in Medicine},
  volume={87},
  number={4},
  pages={1914--1922},
  year={2022},
  publisher={Wiley Online Library}
}

@inproceedings{lala2019deep,
  title={A deep learning approach for image quality assessment of fetal brain MRI},
  author={Lala, Sayeri and Singh, Nalini and Gagoski, Borjan and Turk, Esra and Grant, P Ellen and Golland, Polina and Adalsteinsson, Elfar}
  booktitle={Proceedings of the International Society for Magnetic Resonance in Medicine},
  year={2019},
}