Generative diffusion model enables quantification of calibration‐free arterial spin labeling perfusion magnetic resonance imaging data in an Alzheimer's disease cohort
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.
Authors
- Clifford R. Jack (ORCID: https://orcid.org/0000-0001-7916-622X)
- Steven Cen (ORCID: https://orcid.org/0000-0002-7859-8909)
- Hosung Kim (ORCID: https://orcid.org/0000-0002-2269-8644)
- Nan‐kuei Chen (ORCID: https://orcid.org/0000-0001-6564-4219)
- Qinyang Shou (ORCID: https://orcid.org/0000-0002-3343-3895)
- Matthew L. Senjem (ORCID: https://orcid.org/0000-0001-9308-9275)
- Bret Borowski
- Danny J.J. Wang (ORCID: https://orcid.org/0000-0002-0840-7062)
- John M. Ringman (ORCID: https://orcid.org/0000-0001-5429-0777)
- Arvin Arani (ORCID: https://orcid.org/0000-0001-6607-8781)
Institutions
- University of Southern California (US)
- Mayo Clinic (US)
- University of Arizona (US)
- Cognitive Neuroimaging Lab (FR)
Publication Details
- Journal
- Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1002/dad2.70214
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health