Progressive Feedback Residual Attention Network for Cardiac Magnetic Resonance Imaging Super-Resolution.

Abstract

Atrial fibrillation (AF) is an increasing medical burden worldwide, and its pathological manifestations are atrial tissue remodeling and low-pressure atrial tissue fibrosis. Due to the inherent defects of medical image data acquisition systems, the acquisition of high-resolution cardiac magnetic resonance imaging (CMRI) faces many problems. In response to these problems, we propose the Progressive Feedback Residual Attention Network (PFRN) for CMRI super-resolution. Specifically, we directly perform feature extraction on low-resolution images, retain feature information to a large extent, and then build multiple independent progressive feedback modules to extract high-frequency details. To accelerate network convergence and improve image reconstruction quality, we implement the MS-SSIM-L1 loss function. Furthermore, we utilize the residual attention stack module to explore the image's internal relevance and extract the low-resolution image's detailed features. Extensive benchmark evaluation shows that PFRN can improve the detailed information of the image SR reconstruction results, and the reconstructed CMRI has a better visual effect.