# MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better

arXiv: https://arxiv.org/abs/2003.01217 · HTML: https://mlsanity.com/paper/2003.01217/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

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

- [An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation](https://mlsanity.com/paper/2110.14476/) (2021)
- [IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation](https://mlsanity.com/paper/2106.15097/) (2021)
- [Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution](https://mlsanity.com/paper/2109.01664/) (2021)
- [A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI](https://mlsanity.com/paper/2311.11383/) (2023)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing](https://mlsanity.com/paper/2205.11346/) (2022)
- [Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution](https://mlsanity.com/paper/2103.02940/) (2021)
- [ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement](https://mlsanity.com/paper/2503.11787/) (2025)
- [Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture](https://mlsanity.com/paper/2211.15047/) (2022)
- [TEAM PILOT -- Learned Feasible Extendable Set of Dynamic MRI Acquisition Trajectories](https://mlsanity.com/paper/2409.12777/) (2024)
- [Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning](https://mlsanity.com/paper/2111.11660/) (2021)
- [Enhance the Image: Super Resolution using Artificial Intelligence in MRI](https://mlsanity.com/paper/2406.13625/) (2024)
- [Learning Two-factor Representation for Magnetic Resonance Image Super-resolution](https://mlsanity.com/paper/2409.09731/) (2024)
- [Multi PILOT: Learned Feasible Multiple Acquisition Trajectories for Dynamic MRI](https://mlsanity.com/paper/2303.07150/) (2023)
- [VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution](https://mlsanity.com/paper/2603.23153/) (2026)

## Abstract (arXiv)

High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical information that is critical for diagnosis in the clinical application. However, HR MRI typically comes at the cost of long scan time, small spatial coverage, and low signal-to-noise ratio (SNR). Recent studies showed that with a deep convolutional neural network (CNN), HR generic images could be recovered from low-resolution (LR) inputs via single image super-resolution (SISR) approaches. Additionally, previous works have shown that a deep 3D CNN can generate high-quality SR MRIs by using learned image priors. However, 3D CNN with deep structures, have a large number of parameters and are computationally expensive. In this paper, we propose a novel 3D CNN architecture, namely a multi-level densely connected super-resolution network (mDCSRN), which is light-weight, fast and accurate. We also show that with the generative adversarial network (GAN)-guided training, the mDCSRN-GAN provides appealing sharp SR images with rich texture details that are highly comparable with the referenced HR images. Our results from experiments on a large public dataset with 1,113 subjects showed that this new architecture outperformed other popular deep learning methods in recovering 4x resolution-downgraded images in both quality and speed.