Abstract

Info

Title:: Super-resolution reconstruction network based on implicit degradation model for magnetic resonance images

Author(s):: LIU Huanyu1; GUO Haipeng1; LIU Xiaodong2; LI Han1; LI Junbao1; 1.Information Countermeasure Technique Institute, Faculty of Computing, Harbin Institute of Technology, Harbin 150080, China 2. Department of Automatic Test and Control, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China

Keywords:: Keywords: brain magnetic resonance imaging super-resolution diffusion model

Abstract:: Abstract: Given that the existing methods of enhancing the resolution of magnetic resonance (MR) images by algorithms mainly focus on cross-size and same-size supervised super-resolution algorithms, a super-resolution reconstruction network (SG-Diffusion) for MR images is proposed based on an implicit degradation mapping model. The degradation process of MR images is implicitly modeled through a masked autoencoder, which reduces the domain gap between the experimental constructed dataset and the actual MR images, and the sample pairs are generated based on implicit degradation model. After training, a MR image reconstruction network based on self-guided diffusion model is obtained to realize the spatial resolution enhancement of unsupervised same-size MR images. The results of super-resolution experiments of 4-fold accelerated sampling brain MR images on fastMRI dataset show that the MR image super-resolution reconstruction network based on implicit degradation model proposed in the study can effectively improve the spatial resolution of degraded MR images, and that compared with the image degradation reconstruction method based on the explicit degradation model, the proposed SG-Diffusion method achieves better reconstruction results.