Keywords: Arterial Spin Labelling, Arterial spin labelling, Super-resolution, Latent diffusion model, T1-weight image
Motivation: Arterial Spin Labeling (ASL) imaging suffers from low SNR, low resolution, and long acquisition times, hindering its clinical applications.
Goal(s): To propose a self-supervised ASL super-resolution framework that utilizes a 3D latent and image-space diffusion model.
Approach: A 3D latent space conditional diffusion model was trained using multimodal images, including T1w and ASL. The ASL super-resolution model leverages latent space information from T1w. The method was tested on ASLs acquired at low and high resolutions.
Results: The proposed model provides super-resolution ASL with enhanced details, improved SNR, high visual scores. It was more efficient than the previous ASL diffusion model.
Impact: The proposed method achieved ASL super-resolution by combining latent and image-space models, which can enhance the resolution of 4mm ASL to 2.5mm, equivalent to reducing the scan time by 13 mins.
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