A Deep Autoencoder for Fast Spectral-Temporal Fitting of Dynamic Deuterium Metabolic Imaging Data at 7T
This repository contains the source code implementing the proposed model from our article "A Deep Autoencoder for Fast Spectral-Temporal Fitting of Dynamic Deuterium Metabolic Imaging Data at 7T"
Clone and install the repository using
git clone git@github.com:Osburg/dynamic_fitting.git
cd dynamic_fitting
pip install -e .
Additionally, the dependency (torchcubicspline)[https://github.com/patrick-kidger/torchcubicspline] must be installed. This can be done via
pip install git+https://github.com/patrick-kidger/torchcubicspline.git
Before training the model, prepare a basisset and datasets by running the commands
python ./dldf/data_preparation/read_data.py --config ./config/prepare_data.json
python ./dldf/data_preparation/read_basis.py --config ./config/prepare_data.json
where the paths to the dynamic DMI data and the basis signals (as .txt files) are provided in the configuration file ./config/prepare_data.json. Then, start the training pipeline (configured using ./config/config.json) by running
python ./main.py --mode train --config ./config/config.json
If you use this code in your research, please cite the our paper:
@article{OSBURG2025121632,
title = {A deep autoencoder for fast spectral–temporal fitting of dynamic deuterium metabolic imaging data at 7T},
journal = {NeuroImage},
volume = {324},
pages = {121632},
year = {2025},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2025.121632},
url = {https://www.sciencedirect.com/science/article/pii/S1053811925006354},
author = {Aaron Paul Osburg and Amirmohammad Shamaei and Bernhard Strasser and Fabian Niess and Anna Duguid and Viola Bader and Sabina Frese and Lukas Hingerl and Hauke Fischer and William T. Clarke and Georg Langs and Wolfgang Bogner and Stanislav Motyka},
}