文献类型： 会议论文

第一作者： Yan Jin

作者： Yan Jin ¹ ; Jonathan DuBois ² ; Chongyue Zhao ³ ; Liang Zhan ³ ; Audrey Gabelle ¹ ; Neda Jahanshad ⁴ ; Paul M. Thompson ⁴ ; Arie Gafson ¹ ; Shibeshih Belachew ¹ ;

作者机构： 1.Biogen Digital Health, Biogen Inc

2.Research Biomarkers, Biogen Inc

3.Dept. of Electrical and Computer Engineering, University of Pittsburgh

4.Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California

会议名称： International Workshop on Machine Learning in Medical Imaging

主办单位：

页码： 94-103

摘要： 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.

分类号： tp391.41-53