Brain MRI to PET Synthesis and Amyloid Estimation in Alzheimer's Disease via 3D Multimodal Contrastive GAN

Yan Jin, Jonathan DuBois, Chongyue Zhao, Liang Zhan, Audrey Gabelle, Neda Jahanshad, Paul M. Thompson, Arie Gafson, Shibeshih Mitiku Belachew

Abstract: Positron emission tomography (PET) can detect brain amyloid-β (Aβ) deposits, a diagnostic hallmark of Alzheimer’s disease and a target for disease modifying treatment. However, PET-Aβ is expensive, not widely available, and, unlike magnetic resonance imaging (MRI), exposes the patient to ionizing radiation. Here we propose a novel 3D multimodal generative adversarial network with contrastive learning to synthesize PET-Aβ images from cheaper, more accessible, and less invasive MRI scans (T1-weighted and fluid attenuated inversion recovery [FLAIR] images). In tests on independent samples of paired MRI/PET-Aβ data, our synthetic PET-Aβ images were of high quality with a structural similarity index measure of 0.94, which outperformed previously published methods. We also evaluated synthetic PET-Aβ images by extracting standardized uptake value ratio measurements. The synthetic images could identify amyloid positive patients with a balanced accuracy of 79%, holding promise for potential future use in a diagnostic clinical setting.