Generative diffusion model enables quantification of calibration-free arterial spin labeling perfusion magnetic resonance imaging data in an Alzheimer's disease cohort.

ABSTRACT:

Introduction

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

Methods

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).

Results

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 decreasin

SUBMITTER: Shou Q 

PROVIDER: S-EPMC12572821 | biostudies-literature | 2025 Oct-Dec

REPOSITORIES: biostudies-literature