This repository contains the implementation of the methods described in the paper by Hadas Ben-Atya and Moti Freiman:
"P2T2: A physically-primed deep-neural-network approach for robust T2 distribution estimation from quantitative T2-weighted MRI," Computerized Medical Imaging and Graphics, Volume 107, 2023, 102240, ISSN 0895-6111.
This project focuses on the robust estimation of T2 distributions from quantitative T2-weighted MRI data using deep learning approaches described in the P2T2 and MIML papers. The repository includes scripts for simulating Echo Planar Graphs (EPGs) and training a model to reconstruct T2 distributions.
Before running the simulations and the model, ensure you have the following installed:
- Python 3.8 or later
- NumPy
- PyTorch
- Any other dependencies listed in
requirements.txt
Install the required packages using:
pip install -r requirements.txtUse the script data_simulation.py to simulate MRI data. You can specify the type of model (MIML or P2T2) to determine the simulation parameters.
python data_simulation.py --model_type <MODEL_TYPE> --min_TE <MIN_TE> --max_TE <MAX_TE> --n_echoes <N_ECHOES>MODEL_TYPE: Type of the model ('MIML' for single TE sequence or 'P2T2' for varied TE sequences).MIN_TE: Minimum echo time in milliseconds.MAX_TE: Maximum echo time in milliseconds (only for 'P2T2' type).N_ECHOES: Number of echo times (applies to 'MIML').
To train the model, configure the settings in config.yaml and run pt2_reconstruction_model_main.py.
Edit config.yaml to set various parameters like batch size, learning rate, epochs, etc., according to your computational resources and requirements.
Run the model using:
python pt2_reconstruction_model_main.py --config config.yamlThis project is licensed under the MIT License - see the LICENSE file for details.
If you use this tool in your research, please cite the following paper:
Hadas Ben-Atya, Moti Freiman, "P2T2: A physically-primed deep-neural-network approach for robust T2 distribution estimation from quantitative T2-weighted MRI," Computerized Medical Imaging and Graphics, Volume 107, 2023, 102240, ISSN 0895-6111.
The study was supported in part by research grants from the United States Israel Bi-national Science Foundation (BSF), the Israel Innovation Authority, the Israel Ministry of Science and Technology, and the Microsoft Israel and Israel Inter-University Computation Center program . We thank Thomas Yu, Erick Jorge Canales Rodriguez, Marco Pizzolato, Gian Franco Piredda, Tom Hilbert, Elda Fischi-Gomez, Matthias Weigel, Muhamed Barakovic, Meritxell Bach-Cuadra, Cristina Granziera, Tobias Kober, and Jean-Philippe Thiran, from Yu et al. (2021) for sharing their synthetic data generator with us. We also thank Prof. Noam Ben-Eliezer and the Lab for Advanced MRI at Tel-Aviv University for sharing the real MRI data with us.