# Brain MRI super-resolution using 3D generative adversarial networks

arXiv: https://arxiv.org/abs/1812.11440 · HTML: https://mlsanity.com/paper/1812.11440/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2018.

## Built on by (most cited first, 15 of 15)

- [SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks](https://mlsanity.com/paper/2106.02599/) (2021)
- [Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2303.14739/) (2023)
- [Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis](https://mlsanity.com/paper/2112.10325/) (2021)
- [Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks](https://mlsanity.com/paper/2310.01251/) (2023)
- [I3Net: Inter-Intra-slice Interpolation Network for Medical Slice Synthesis](https://mlsanity.com/paper/2405.02857/) (2024)
- [Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks](https://mlsanity.com/paper/2104.12510/) (2021)
- [Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation](https://mlsanity.com/paper/2107.07975/) (2021)
- [A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning](https://mlsanity.com/paper/2302.11184/) (2023)
- [Manifold-aware Synthesis of High-resolution Diffusion from Structural Imaging](https://mlsanity.com/paper/2108.04135/) (2021)
- [Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning](https://mlsanity.com/paper/2110.03452/) (2021)
- [Generation of Synthetic Rat Brain MRI scans with a 3D Enhanced Alpha-GAN](https://mlsanity.com/paper/2112.13626/) (2021)
- [Single MR Image Super-Resolution using Generative Adversarial Network](https://mlsanity.com/paper/2207.08036/) (2022)
- [Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques](https://mlsanity.com/paper/2209.09880/) (2022)
- [VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution](https://mlsanity.com/paper/2603.23153/) (2026)
- [Structure-Adaptive Sparse Diffusion in Voxel Space for 3D Medical Image Enhancement](https://mlsanity.com/paper/2604.17773/) (2026)

## Abstract (arXiv)

In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D convolutions to exploit volumetric information. For the discriminator, the adversarial loss uses least squares in order to stabilize the training. For the generator, the loss function is a combination of a least squares adversarial loss and a content term based on mean square error and image gradients in order to improve the quality of the generated images. We explore different solutions for the upsampling phase. We present promising results that improve classical interpolation, showing the potential of the approach for 3D medical imaging super-resolution. Source code available at https://github.com/imatge-upc/3D-GAN-superresolution