Abstract #0559

MR fingerprinting and complex-valued neural network for quantification of brain amyloid burden

Shohei Fujita^1,2, Yujiro Otsuka^1,3,4, Katsutoshi Murata⁵, Gregor Koerzdoerfer⁶, Mathias Nittka⁶, Yumiko Motoi^7,8, Madoka Nakajima^8,9, Koji Murakami¹⁰, Issei Fukunaga¹, Koji Kamagata¹, Osamu Abe², and Shigeki Aoki¹

¹Department of Radiology, Juntendo University, Tokyo, Japan, ²Department of Radiology, The University of Tokyo, Tokyo, Japan, ³Milliman Inc, Tokyo, Japan, ⁴Plusman LLC, Tokyo, Japan, ⁵Siemens Healthcare Japan KK, Tokyo, Japan, ⁶Siemens Healthcare GmbH, Erlangen, Germany, ⁷Department of Neurology, Juntendo University, Tokyo, Japan, ⁸Medical Center for Dementia, Juntendo University, Tokyo, Japan, ⁹Department of Neurosurgery, Juntendo University, Tokyo, Japan, ¹⁰Division of Nuclear Medicine, Department of Radiology, Juntendo University, Tokyo, Japan

Synopsis

We developed a framework utilizing MR fingerprinting and a complex-valued neural network to detect brain amyloid burden. The tailored neural network was trained on real amyloid-PET imaging data and MR fingerprinting acquisitions to estimate PET-derived amyloid deposition from the MR fingerprinting signal evolutions. This complex-valued neural network architecture, designed to increase sensitivity to amyloid-related signals, showed a subject-level amyloid positivity classification with AUC = 0.87 in patients with cognitive decline. The proposed method enables non-invasive amyloid burden mapping, T1 and T2 mapping in a single scan, and is suitable not only for diagnosis but also for monitoring amyloid-reducing treatments.

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