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

arXiv: https://arxiv.org/abs/1907.04835 · HTML: https://mlsanity.com/paper/1907.04835/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 27 of 27)

- [An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation](https://mlsanity.com/paper/2110.14476/) (2021)
- [Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models](https://mlsanity.com/paper/2111.14923/) (2021)
- [DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2303.13933/) (2023)
- [CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution](https://mlsanity.com/paper/2303.16242/) (2023)
- [InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model](https://mlsanity.com/paper/2308.12465/) (2023)
- [Memory-augmented Deep Unfolding Network for Guided Image Super-resolution](https://mlsanity.com/paper/2203.04960/) (2022)
- [SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks](https://mlsanity.com/paper/2106.02599/) (2021)
- [CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data](https://mlsanity.com/paper/2404.04878/) (2024)
- [Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution](https://mlsanity.com/paper/2109.01664/) (2021)
- [Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS](https://mlsanity.com/paper/2503.01075/) (2025)
- [TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution](https://mlsanity.com/paper/2306.06669/) (2023)
- [SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution](https://mlsanity.com/paper/2406.03359/) (2024)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images](https://mlsanity.com/paper/2210.05117/) (2022)
- [Self-supervised arbitrary scale super-resolution framework for anisotropic MRI](https://mlsanity.com/paper/2305.01360/) (2023)
- [Embedding Hallucination for Few-Shot Language Fine-tuning](https://mlsanity.com/paper/2205.01307/) (2022)
- [Generalized super-resolution 4D Flow MRI unicodex2013 using ensemble learning to extend across the cardiovascular system](https://mlsanity.com/paper/2311.11819/) (2023)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging](https://mlsanity.com/paper/2104.05901/) (2021)
- [Enhance the Image: Super Resolution using Artificial Intelligence in MRI](https://mlsanity.com/paper/2406.13625/) (2024)
- [Single MR Image Super-Resolution using Generative Adversarial Network](https://mlsanity.com/paper/2207.08036/) (2022)
- [Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation](https://mlsanity.com/paper/2502.08634/) (2025)
- [SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction](https://mlsanity.com/paper/2505.17472/) (2025)
- [MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction](https://mlsanity.com/paper/2603.03710/) (2026)
- [PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution](https://mlsanity.com/paper/2607.06238/) (2026)
- [Anatomically Consistent Cross-Contrast Super-Resolution of Anisotropic Brain T2w MRI](https://mlsanity.com/paper/2608.08401/) (2026)
- [UniH3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2609.11156/) (2026)

## Abstract (arXiv)

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.