# Enhanced generative adversarial network for 3D brain MRI super-resolution

## TL;DR

This paper introduces an enhanced GAN-based method for 3D brain MRI super-resolution, featuring a novel residual-in-residual dense generator and improved discriminator, achieving state-of-the-art quantitative performance and better anatomical fidelity.

## Contribution

The work presents a new residual-in-residual dense generator and a patch GAN discriminator for 3D MRI super-resolution, improving texture detail recovery and anatomical accuracy.

## Key findings

- Achieved state-of-the-art PSNR, SSIM, and NRMSE metrics.
- Enhanced convergence and texture modeling in GAN discriminator.
- Validated improved anatomical fidelity with a pre-trained brain parcellation network.

## Abstract

Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data. Generative Adversarial Networks (GAN) have proven to perform well in recovering image texture detail, and many variants have therefore been proposed for SISR. In this work, we develop an enhancement to tackle GAN-based 3D SISR by introducing a new residual-in-residual dense block (RRDG) generator that is both memory efficient and achieves state-of-the-art performance in terms of PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and NRMSE (Normalized Root Mean Squared Error) metrics. We also introduce a patch GAN discriminator with improved convergence behavior to better model brain image texture. We proposed a novel the anatomical fidelity evaluation of the results using a pre-trained brain parcellation network. Finally, these developments are combined through a simple and efficient method to balance etween image and texture quality in the final output.