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Abstract #3911

MR fingerprinting for quantification of brain amyloid burden: from development to prospective multi-site external validation

Shohei Fujita1,2,3,4, Yasutaka Fushimi5, Yujiro Otsuka1,6,7, Katsutoshi Murata8, Guido Buonincontri9, Gregor Koerzdoerfer10, Mathias Nittka9, Issei Fukunaga1, Kaito Takabayashi1, Yumiko Motoi11,12, Madoka Nakajima12,13, Koji Murakami14, Atsushi Shima15, Manabu Kubota16, Berkin Bilgic3,4,17, Koji Kamagata1, Nobukatsu Sawamoto18, Osamu Abe2, Yuji Nakamoto5, and Shigeki Aoki1
1Dept. of Radiology, Juntendo University, Tokyo, Japan, 2Dept. of Radiology, The University of Tokyo, Tokyo, Japan, 3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, 4Dept. of Radiology, Harvard Medical School, Boston, MA, United States, 5Dept. of Diagnostic Imaging and Nuclear Medicine, Kyoto University, Kyoto, Japan, 6Milliman Inc, Tokyo, Japan, 7Plusman LLC, Tokyo, Japan, 8Siemens Healthcare Japan KK, Tokyo, Japan, 9Siemens Healthcare GmbH, Erlangen, Germany, 10Siemens Medical Solutions, New York, NY, United States, 11Dept. of Neurology, Juntendo University, Tokyo, Japan, 1212. Medical Center for Dementia, Juntendo University, Tokyo, Japan, 13Dept. of Neurosurgery, Juntendo University, Tokyo, Japan, 14Division of Nuclear Medicine, Dept. of Radiology, Juntendo University, Tokyo, Japan, 15Department of Regenerative Systems Neuroscience, Human Brain Research Center, Kyoto University, Kyoto, Japan, 16Dept. of Psychiatry, Kyoto University, Kyoto, Japan, 17Harvard/MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, 18Dept. of Human Health Sciences, Kyoto University, Kyoto, Japan

Synopsis

Keywords: Alzheimer's Disease, Alzheimer's Disease, Biomarker

Motivation: A non-invasive amyloid beta (Aβ) imaging technique is needed for objective diagnosis and treatment monitoring of Alzheimer’s disease.

Goal(s): To develop and validate an MRF-based method quantifying brain Aβ.

Approach: A framework with efficient MRF data acquisition, neural network decoding, and atlas-based segmentation was implemented. A prospective analysis was conducted on external dataset to evaluate its generalizability, repeatability, and correlation with Aβ-PET measurements and clinical cognitive function tests.

Results: The method showed high repeatability (CV<2%), significant correlation with Aβ-PET measurements and Montreal Cognitive Assessment test (p=0.015 and 0.020, respectively), and discriminated subject-level Aβ positivity with an AUC of 0.84 on external test set.

Impact: The proposed framework is compatible with clinical 3T MRI and offers ‘one-stop’ examination in 10 minutes for patients with cognitive decline by providing structural MRI and Aβ-quantification. Its non-invasive nature facilitates longitudinal evaluation and correlates with Aβ-PET and cognitive function.

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Abstract

Keywords