Single MR Image Super-Resolution using Generative Adversarial Network

TL;DR

This paper adapts the Real-ESRGAN super-resolution method to enhance the spatial resolution of 2D MRI images, demonstrating improved image quality through qualitative and quantitative validation.

Contribution

The study introduces a modified Real-ESRGAN architecture tailored for 2D MRI images, applying it to brain tumor data for improved super-resolution performance.

Findings

Enhanced MRI resolution validated by SSIM, NRMSE, MAE, VIF metrics.

Qualitative assessments show clearer, more detailed MRI images.

Quantitative results confirm the effectiveness of the modified approach.

Abstract

Spatial resolution of medical images can be improved using super-resolution methods. Real Enhanced Super Resolution Generative Adversarial Network (Real-ESRGAN) is one of the recent effective approaches utilized to produce higher resolution images, given input images of lower resolution. In this paper, we apply this method to enhance the spatial resolution of 2D MR images. In our proposed approach, we slightly modify the structure of the Real-ESRGAN to train 2D Magnetic Resonance images (MRI) taken from the Brain Tumor Segmentation Challenge (BraTS) 2018 dataset. The obtained results are validated qualitatively and quantitatively by computing SSIM (Structural Similarity Index Measure), NRMSE (Normalized Root Mean Square Error), MAE (Mean Absolute Error), and VIF (Visual Information Fidelity) values.