Anatomy guided truncated conditional diffusion model for super-resolution arterial spin labeling imaging

Arterial Spin Labeling (ASL) is a non-invasive magnetic resonance imaging technique used to measure cerebral blood flow. However, ASL images typically suffer from low resolution and long acquisition times because of the low signal-to-noise ratio. Therefore, high-resolution ASL images are challenging to acquire but highly desirable. This work proposes a super-resolution method for ASL based on an anatomy guided truncated conditional diffusion model, including an anatomy guided data synthesizer, an anatomy guided truncated diffusion module, and a two-stage inference module. The proposed model was developed and validated on retrospective, prospective, and clinical datasets. Its performance was compared against both conventional and deep learning methods. The proposed model demonstrates superior performance, including 2-18% gains in SSIM and 26-50% reductions in FID on the prospectively acquired data, compared to existing methods. The results suggest that the method offers a promising approach for achieving high-resolution ASL imaging. The proposed model is available on GitHub: https://github.com/yunzxu/ATCDM.