# GRAPPA-GANs for Parallel MRI Reconstruction

arXiv: https://arxiv.org/abs/2101.03135 · HTML: https://mlsanity.com/paper/2101.03135/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Natural Language Processing Advancements By Deep Learning: A Survey](https://mlsanity.com/paper/2003.01200/) (2020)
- [Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction](https://mlsanity.com/paper/1908.09262/) (2019)
- [Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks](https://mlsanity.com/paper/1910.06067/) (2019)
- [Design and Implementation of a Maxi-Sized Mobile Robot (Karo) for Rescue Missions](https://mlsanity.com/paper/2008.10396/) (2020)
- [Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer](https://mlsanity.com/paper/2006.15264/) (2020)
- [Analysis and Control of Fiber-Reinforced Elastomeric Enclosures (FREEs)](https://mlsanity.com/paper/1912.07380/) (2019)
- [A Self-Decoupled 32 Channel Receive Array for Human Brain Magnetic Resonance Imaging at 10.5T](https://mlsanity.com/paper/2009.07163/) (2020)

## Built on by (most cited first, 2 of 2)

- [An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities](https://mlsanity.com/paper/2109.09738/) (2021)
- [Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information](https://mlsanity.com/paper/2112.05758/) (2021)

## Abstract (arXiv)

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.