Welcome to the Official Repository of PCMC-T1.

PCMC-T1: Free-breathing Myocardial T1 Mapping with Physically-Constrained Motion Correction, MICCAI 2023

What is PCMC-T1?

$T_1$ mapping is a quantitative magnetic resonance imaging (qMRI) technique that has emerged as a valuable tool in the diagnosis of diffuse myocardial diseases. However, prevailing approaches have relied heavily on breath-hold sequences to eliminate respiratory motion artifacts. This limitation hinders accessibility and effectiveness for patients who cannot tolerate breath-holding. Image registration can be used to enable free-breathing $T_1$ mapping. Yet, inherent intensity differences between the different time points make the registration task challenging. We introduce PCMC-T1, a physically-constrained deep-learning model for motion correction in free-breathing $T_1$ mapping. We incorporate the signal decay model into the network architecture to encourage physically-plausible deformations along the longitudinal relaxation axis.

If you use PCMC-T1 in your research, please cite the paper as follows:

E. Hanania, I. Volovik, L. Barkat, I. Cohen, and M. Freiman, PCMC-T1: Free-Breathing Myocardial T1 Mapping with
Physically Constrained Motion Correction, Proc. 26th International Conference on Medical Image Computing and
Computer Assisted Intervention, MICCAI 2023, Vancouver, Canada, Oct. 8-12, 2023.

Installation

To use this project, follow these steps:

1. Clone the Repository:

   git clone https://github.com/eyalhana/PCMC-T1.git

2. Install Dependecies

   pip install -r requirements.txt
   

Dataset

We utilized a publicly available myocardial T1 mapping dataset. To use this dataset, please download it in '.mat' format: Use wget (Linux/macOS) or curl (Windows/Linux/macOS) to download the file:

• On Linux/macOS, use wget:

  wget -O data/T1Dataset210.mat [https://dataverse.harvard.edu/api/access/datafile/43188520](https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/DHEUAV/H5WVDS)

• On Windows/Linux/macOS, use curl:

  curl -o data/T1Dataset210.mat [https://dataverse.harvard.edu/api/access/datafile/43188520](https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/DHEUAV/H5WVDS)

Usage instructions

Training

To train using the MICCAI 2023 configuration (physically-constrained motion correction):

python /scripts/training.py --config /configs/miccai.yaml --fold 1

To train using the ISMRM 2023 configuration (group-wise mutual-information based motion correction):

python /scripts/training.py --config /configs/ismrm.yaml --fold 1

You can customize hyperparameters and file paths in the configuration files.

Customizing Data Loading

If you intend to train your own model using custom datasets or data formats, you'll likely need to customize the data-loading code in the following project files:

• src/generators.py
• src/data_preprocessing.py
• config_loader.py

Testing

We used the $R^2$ of the model fit to the observed data in the myocardium, the Dice score, and Hausdorff distance values of the myocardium segmentations as the evaluation metrics. To assess the quality of a model by computing these metrics, run:

python /scripts/evaluation.py --config configs/miccai.yaml --model models/model_name.pt

Contact

For any code-related problems or questions please open an issue. For other inquiries, please contact us via email at eyalhan at campus.technion.ac.il.