This repository contains a PyTorch implementation of the paper:

PILOT: Physics-Informed Learned Optimal Trajectories for Accelerated MRI.

Tomer Weiss ([email protected]), Ortal Senouf, Sanketh Vedula, Oleg Michailovich, Michael Zibulevsky, Alex Bronstein

• v2 - new! work with multi-channel data.

Magnetic Resonance Imaging (MRI) has long been considered to be among “the gold standards” of diagnostic medical imaging. The long acquisition times, however, contribution to the relative high costs of MRI examination. Over the last few decades, multiple studies have focused on the development of both physical and post-processing methods for accelerated acquisition of MRI scans. In this work, we propose a novel approach to the learning of optimal schemes for conjoint acquisition and reconstruction of MRI scans, with the optimization carried out simultaneously. To be of a practical value, the schemes are encoded in the form of general k-space trajectories, whose associated magnetic gradients are constrained to obey a set of predefined hardware requirements (as defined in terms of, e.g., peak currents and maximum slew rates of magnetic gradients). We demonstrate the effectiveness of the proposed solution in application to both image reconstruction and image segmentation, reporting substantial improvements in terms of acceleration factors as well as the quality of these end tasks.

This repo contains the codes to replicate our experiment for reconstruction.

To install other requirements through $ pip install -r requirements.txt.

First you should download the multicoil dataset from fastMRI and split the training + validation sets to training + validation + test set. Update the datasets location in common/arg.py. We provide script to easily run experiment, fill free to change the parameters as needed.

$ python exp.py

Please cite our work if you find this approach useful in your research:

@article{weiss2021pilot,
  title={{PILOT}: Physics-Informed Learned Optimized Trajectories for Accelerated {MRI}},
  author={Weiss, Tomer and Senouf, Ortal and Vedula, Sanketh and Michailovich, Oleg and Zibulevsky, Michael and Bronstein, Alex and others},
  journal={Machine Learning for Biomedical Imaging},
  year={2021}
}

We use the fastMRI as starter template. We also used Sigpy as base to our pytorch-nufft implementation.