Generative diffusion model enables quantification of calibration-free arterial spin labeling perfusion magnetic...

M0 images were missing in Siemens ASL data in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, prohibiting cerebral blood flow (CBF) quantification.

A conditional latent diffusion model was trained and evaluated on in-house datasets, then applied to the Siemens data in ADNI-3. Regional CBF differences by Alzheimer's disease (AD) stages, their accuracy for AD classification, and CBF trajectory slopes were compared between generated data (Siemens) and acquired data (General Electric).

The diffusion model generated M0 images with high fidelity (SSIM = 0.918 ± 0.023, PSNR = 31.361 ± 2.537) and minimal CBF bias (mean difference is 0.21 ± 1.58 mL/100 g/min). Both generated and acquired CBF showed similar spatial patterns and decreasing trends with AD progression in specific AD-related regions. Generated CBF also improved accuracy in classifying AD stages compared to qualitative perfusion images.

This study shows the potential of diffusion models for imputing missing modalities in large-scale studies exploring the use of ASL as a biomarker of AD.

Using latent diffusion model, we can generate M0 image from control image in arterial spin labeling (ASL) with high fidelity.The generated M0 can be used for cerebral blood flow (CBF) quantification in Alzheimer's Disease Neuroimaging Initiative dataset.The performance of classification between Alzheimer's disease (AD) patients and cognitive normal people is better when using generated CBF maps than using non-quantitative perfusion images.ASL CBF decreases with AD progression in key AD-related brain regions.