# Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction

arXiv: https://arxiv.org/abs/1908.09262 · HTML: https://mlsanity.com/paper/1908.09262/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2019.

## Built on by (most cited first, 8 of 8)

- [Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution](https://mlsanity.com/paper/2203.13963/) (2022)
- [GRAPPA-GANs for Parallel MRI Reconstruction](https://mlsanity.com/paper/2101.03135/) (2021)
- [A Comprehensive Survey on Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2503.07097/) (2025)
- [Specular reflections removal in colposcopic images based on neural networks: Supervised training with no ground truth previous knowledge](https://mlsanity.com/paper/2106.02221/) (2021)
- [Biomedical Image Reconstruction: A Survey](https://mlsanity.com/paper/2301.11813/) (2023)
- [Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review](https://mlsanity.com/paper/2303.09012/) (2023)
- [TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling](https://mlsanity.com/paper/2408.05705/) (2024)
- [AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI Synthesis](https://mlsanity.com/paper/2502.02555/) (2025)

## Abstract (arXiv)

Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In order to accelerate the acquisition, multiple deep learning networks have been proposed. Recently, Generative Adversarial Networks (GANs) have shown promising results in MRI reconstruction. The drawback with the proposed GAN based methods is it does not incorporate the prior information about the end goal which could help in better reconstruction. For instance, in the case of cardiac MRI, the physician would be interested in the heart region which is of diagnostic relevance while excluding the peripheral regions. In this work, we show that incorporating prior information about a region of interest in the model would offer better performance. Thereby, we propose a novel GAN based architecture, Reconstruction Global-Local GAN (Recon-GLGAN) for MRI reconstruction. The proposed model contains a generator and a context discriminator which incorporates global and local contextual information from images. Our model offers significant performance improvement over the baseline models. Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance. We also demonstrate that the reconstructions from the proposed method give segmentation results similar to fully sampled images.