PHIMO: Physics-Informed Motion Correction of GRE MRI for T2* Quantification

Hannah Eichhorn, Kerstin Hammernik, Veronika Spieker, Elisa Saks, Kilian Weiss, Christine Preibisch, Julia A. Schnabel

• Accepted as oral at ISMRM 2024 | abstract

• Extension accepted at MICCAI 2024 | find the repository here

Synopsis: Motivation: T2*-quantification from GRE-MRI is particularly impacted by subject motion due to its sensitivity to magnetic field inhomogeneities. The current multi-parametric quantitative BOLD motion correction method depends on additional k-space acquisition, extending overall acquisition times. Goal(s): To develop a learning-based motion correction method tailored to T2*-quantification that avoids redundant data acquisition. Approach: PHIMO leverages multi-echo T2*-decay information to identify motion-corrupted k-space lines and exclude them from a data-consistent deep learning reconstruction. Results: We are able to correct motion artifacts in subjects with stronger motion, approaching the performance of the current motion correction method, while substantially reducing the acquisition time. Impact: PHIMO reduces strong motion artifacts in T2* maps by utilizing T2* decay information in an unrolled DL reconstruction. PHIMO avoids redundant data acquisition compared to a current correction method and reduces the acquisition time by over 40%, facilitating clinical applicability

Citation

If you use this code, please cite our abstract:

@InProceedings{eichhorn2024ismrm,
      title={PHIMO: Physics-Informed Motion Correction of {GRE} {MRI} for {T2*} Quantification}, 
      author={Hannah Eichhorn and Kerstin Hammernik and Veronika Spieker and Elisa Sacks and Kilian Weiss and Christine Preibisch and Julia A. Schnabel},
      booktitle="Proceedings of the 2024 ISMRM & ISMRT Annual Meeting & Exhibition",
      year={2024},
}

Contents of this repository:

• iml-dl: code belonging to the above work, using an adapted version of the IML-CompAI Framework and the MERLIN Framework

All computations were performed using Python 3.8.12 and PyTorch 2.0.1.

Setup:

1. Create a virtual environment with the required packages:

   cd ${TARGET_DIR}/PHIMO
   conda env create -f conda_environment.yml
   source activate phimo *or* conda activate phimo
   

2. Install pytorch with cuda:

   conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
   pip install torchinfo
   conda install -c conda-forge pytorch-lightning
   

3. For setting up wandb please refer to the IML-CompAI Framework.

Steps to reproduce the analysis:

1. Use the file ismrm-abstract/iml-dl/projects/recon_t2star/preprocessing/create_symbolic_links.py to create symbolic links under ismrm-abstract/iml-dl/data/links_to_data/ (folders need to be adapted here).

2. Adapt ismrm-abstract/iml-dl/projects/recon_t2star/configs/config_training.yaml and ismrm-abstract/iml-dl/projects/recon_t2star/configs/config_inference.yaml to correct settings (i.e. input and output folders)

3. Run the following commands for training (reconstruction):

# go to right directory and activate the conda environment
cd path_to_code/PHIMO/ismrm-abstract/iml-dl
conda activate ismrm_2024

# launch the experiment
echo 'Starting Script'
python -u ./core/Main.py --config_path ./projects/recon_t2star/configs/config_training.yaml

4. Run the following commands for interference (motion correction):

# go to right directory and activate the conda environment
cd path_to_code/PHIMO/ismrm-abstract/iml-dl
conda activate ismrm_2024

# launch the experiment
echo 'Starting Script'
python -u ./core/Main.py --config_path ./projects/recon_t2star/configs/config_inference.yaml

5. For the evaluation of results and creation of plots for ISMRM abstract, adapt the config file ismrm-abstract/iml-dl/projects/recon_t2star/configs/config_eval.yaml and run ismrm-abstract/iml-dl/projects/recon_t2star/evaluate_predictions.py

Illustration of PHIMO: