Super-Resolution of Brain MRI via U-Net Architecture

This paper proposes a U-Net-based deep learning architecture for the task of super-resolution of lower resolution brain magnetic resonance images (MRI). The proposed system, called MRI-Net, is designed to learn the mapping between low-resolution and high-resolution MRI images. The system is trained using 50-800 2D MRI scans, depending on the architecture, and is evaluated using peak signal-to-noise ratio (PSNR) metrics on 10 randomly selected images. The proposed U-Net architecture outperforms current state-of-the-art networks in terms of PSNR when evaluated with a 3 x 3 resolution downsampling index. The system's ability to super-resolve MRI scans has the potential to enable physicians to detect pathologies better and perform a wider range of applications. The symmetrical downsampling pipeline used in this study allows for generically representing low-resolution MRI scans to highlight proof of concept for the U-Net-based approach. The system is implemented on PyTorch 1.9.0 with NVIDIA GPU processing to speed up training time. U-Net is a promising tool for medical applications in MRI, which can provide accurate and high-quality images for better diagnoses and treatment plans. The proposed approach has the potential to reduce the costs associated with high-resolution MRI scans by providing a solution for enhancing the image quality of low-resolution scans.