Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
Brain Amyloid Burden Mapping Using MR Fingerprinting Aided by Deep Learning.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Fujita Shohei, Fushimi Yasutaka, Otsuka Yujiro, Murata Katsutoshi, Buonincontri Guido, Koerzdoerfer Gregor, Nittka Mathias, Fukunaga Issei, Takabayashi Kaito, Hagiwara Akifumi, Motoi Yumiko, Nakajima Madoka, Murakami Koji, Shima Atsushi, Kubota Manabu, Bilgic Berkin, Kamagata Koji, Sawamoto Nobukatsu, Abe Osamu, Nakamoto Yuji, Aoki Shigeki
Аннотация
PURPOSE: 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.
METHODS: 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 B1 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.
RESULTS: 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).
CONCLUSION: 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.
METHODS: 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 B1 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.
RESULTS: 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).
CONCLUSION: 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.