Brain Amyloid Burden Mapping Using MR Fingerprinting Aided by Deep Learning.
Authors
Affiliations (19)
Affiliations (19)
- Department of Radiology, Juntendo University, Tokyo, Japan.
- Department of Radiology, The University of Tokyo, Tokyo, Japan.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston MA, USA.
- Department of Radiology, Harvard Medical School, Boston MA, USA.
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan.
- Biomedical Imaging Research Center, University of Fukui, Eiheiji, Fukui, Japan.
- Milliman Inc., Tokyo, Japan.
- Plusman LLC, Tokyo, Japan.
- Siemens Healthcare K.K., Tokyo, Japan.
- Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers, Erlangen Bavaria, Germany.
- Siemens Medical Solutions, New York NY, USA.
- Department of Neurology, Juntendo University, Tokyo, Japan.
- The Medical Center for Dementia, Juntendo University, Tokyo, Japan.
- Department of Neurosurgery, Juntendo University, Tokyo, Japan.
- Division of Nuclear Medicine, Department of Radiology, Juntendo University, Tokyo, Japan.
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan.
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan.
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan.
- Harvard/MIT Health Sciences and Technology, Cambridge MA, USA.
Abstract
To develop and externally validate a non-invasive framework for quantifying brain amyloid-β (Aβ) deposition using magnetic resonance fingerprinting (MRF) and neural network-based decoding, with positron emission tomography (PET) as the reference standard. This prospective multi-site study included 44 participants from 2 sites who had undergone, or were scheduled to undergo, Aβ PET within 1 year. MRF was performed on a 3T MR system using a 2D fast imaging with steady-state precession sequence with B<sub>1</sub> correction, covering the whole brain in 9.5 min. PET images were co-registered to the MRF space, and regional amyloid load was calculated using an automated template-based pipeline. An inverse mapping function was implemented to convert MRF signals into amyloid burden maps. Repeatability, agreement with PET-based centiloid values, and associations with cognitive scores were evaluated. The generated amyloid maps were visually similar to PET images. Test-retest analysis showed high repeatability, with a coefficient of variation of 1.8 ± 1.3% and an intraclass correlation coefficient of 0.84. In the external test set, MRF-based measurements correlated significantly with PET centiloid scores (Spearman's ρ = 0.589, P = 0.015) and Montreal Cognitive Assessment scores (ρ = -0.543, P = 0.020). The proposed framework enables non-invasive Aβ mapping using a clinically feasible MRI protocol and may support repeated assessment for monitoring during anti-amyloid treatment.