GRAPPA-GANs for Parallel MRI Reconstruction
- URL: http://arxiv.org/abs/2101.03135v2
- Date: Mon, 15 Feb 2021 19:48:12 GMT
- Title: GRAPPA-GANs for Parallel MRI Reconstruction
- Authors: Nader Tavaf, Amirsina Torfi, Kamil Ugurbil, Pierre-Francois Van de
Moortele
- Abstract summary: Reconstruction model combining GeneRalized Autocalibrating Partial Parallel Acquisition(GRAPPA) with a conditional generative adversarial network (GAN) was developed.
For various acceleration rates, GAN and GRAPPA reconstructions were compared in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM)
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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