Deep Learning–Based Prediction of PET Amyloid Status Using MRI
The use of multicontrast MRI, specifically incorporating T2 FLAIR in addition to T1w images, significantly improved the predictive accuracy of PET-determined amyloid status from MRIs by using a deep learning approach.
Authors
- Beng‐Choon Ho (ORCID: https://orcid.org/0000-0003-3976-1555)
- Jon André Ottesen (ORCID: https://orcid.org/0000-0002-5383-2439)
- Greg Zaharchuk (ORCID: https://orcid.org/0000-0001-5781-8848)
- Elizabeth C. Mormino (ORCID: https://orcid.org/0000-0002-4542-6537)
- Christina B. Young (ORCID: https://orcid.org/0000-0001-9535-2137)
- Donghoon Kim (ORCID: https://orcid.org/0000-0001-6945-6952)
- Ashwin Kumar (ORCID: https://orcid.org/0009-0003-8782-1388)
- Elsa Bismuth
Institutions
- Oslo University Hospital (NO)
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-06-27
- DOI
- https://doi.org/10.3174/ajnr.a8899
- Citations
- 1
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36