k-space undersampling is a standard technique to accelerate MR image acquisitions. Reconstruction techniques including GeneRalized Autocalibrating Partial Parallel Acquisition(GRAPPA) and its variants are utilized extensively in clinical and research settings. A reconstruction model combining GRAPPA with a conditional generative adversarial network (GAN) was developed and tested on multi-coil human brain images from the fastMRI dataset. For various acceleration rates, GAN and GRAPPA reconstructions were compared in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). For an acceleration rate of R=4, PSNR improved from 33.88 using regularized GRAPPA to 37.65 using GAN. GAN consistently outperformed GRAPPA for various acceleration rates.
@article{arxiv.2101.03135,
title = {GRAPPA-GANs for Parallel MRI Reconstruction},
author = {Nader Tavaf and Amirsina Torfi and Kamil Ugurbil and Pierre-Francois Van de Moortele},
journal= {arXiv preprint arXiv:2101.03135},
year = {2021}
}