Self-Supervised SUper-Resolution ASL Enhancement based on Conditional Diffusion Models (SURED)

Abstract

Motivation: Arterial spin labeling (ASL) MRI is a non-invasive technique used for measuring perfusion. However, the resolution of ASL is limited by its low SNR. Goal(s): to propose an ASL super-resolution method based on a self-supervised training strategy and the conditional diffusion model. Approach: Synthetic high resolution ASL images were generated by utilizing paired T1w images and low-resolution ASL images. A modified conditional diffusion model was trained to simultaneously achieve resolution enhancement and denoising. The proposed model was tested on simulated and volunteer images. Results: The proposed network demonstrates superior enhanced image details, improved SNR, and preserved original contrast in conventional low-resolution ASL images. Impact: The proposed method enhanced the ASL images without requiring the high-resolution ASL for training. It enables super-resolution ASL images from 4 minutes scans to approach those acquired in 17min.