ResoNet: MRI Super-Resolution via Multi-Path GAN with Adaptive Convolutional Kernels

Motivation: The project aims to improve MRI image resolution, addressing the limitations of current super-resolution methods that hinder accurate medical diagnosis and treatment planning. Goal(s): Enhancing MRI image clarity through ResoNet, an innovative approach featuring a multi-path generator, self-adaptive kernel sizing, and a comprehensive loss function. Approach: Multi-path generator enhances resolution with different kernel sizes, preserving details. Self-adaptive component optimizes kernel size for efficiency. Comprehensive loss function combines GAN-based discriminator, VGG16, and WGAN-GP for sTab. training and quality samples. Results: ResoNet significantly enhances SSIM and PSNR scores, promising substantial impact in medical imaging for more precise diagnoses and treatment planning by healthcare professionals. Impact: This work improves MRI diagnostics by enhancing image resolution, enabling clearer visuals for accurate medical assessments. It optimizes performance and resources, accelerates scanning time, and potentially transforms emergency medical practices, making it an improvement in medical imaging.