Deep neural networks (DNNs) have recently found emerging use in accelerated MRI reconstruction. DNNs typically learn data-driven priors from large datasets constituting pairs of undersampled and fully-sampled acquisitions. Acquiring such large datasets, however, might be impractical. To mitigate this limitation, we propose a few-shot learning approach for accelerated MRI that merges subject-driven priors obtained via physical signal models with data-driven priors obtained from a few training samples. Demonstrations on brain MR images from the NYU fastMRI dataset indicate that the proposed approach requires just a few samples to outperform traditional parallel imaging and DNN algorithms.
@article{arxiv.2103.07790,
title = {A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors},
author = {Salman Ul Hassan Dar and Mahmut Yurt and Tolga Çukur},
journal= {arXiv preprint arXiv:2103.07790},
year = {2021}
}
Comments
Accepted for presentation at the 29th Annual Meeting of the International Society of Magnetic Resonance in Medicine (ISMRM)