Self-supervised super-resolution of 2-D pre-clinical MRI acquisitions

Abstract

Animal models are pivotal in disease research and the advancement of therapeutic methods. The translation of results from these models to clinical applications is enhanced by employing technologies which are consistent for both humans and animals, like Magnetic Resonance Imaging (MRI), offering the advantage of longitudinal disease evaluation without compromising animal welfare. However, current animal MRI techniques predominantly employ 2D acquisitions due to constraints related to organ size, scan duration, image quality, and hardware limitations. While 3D acquisitions are feasible, they are constrained by longer scan times and ethical considerations related to extended sedation periods. This study evaluates the efficacy of SMORE, a self-supervised deep learning super-resolution approach, to enhance the through-plane resolution of anisotropic 2D MRI scans into isotropic resolutions. SMORE accomplishes this by self-training with high-resolution in-plane data, thereby eliminating domain discrepancies between the input data and external training sets. The approach is tested on mouse MRI scans acquired across a range of through-plane resolutions. Experimental results show SMORE substantially outperforms traditional interpolation methods. Additionally, we find that pre-training offers a promising approach to reduce processing time without compromising performance.