Park Jongwan, Hagan Kristen, DuBose Theodore B, Maldonado Ramiro S, McNabb Ryan P, Dubra Alfredo, Izatt Joseph A, Farsiu Sina
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA.
Sci Adv. 2025 May 9;11(19):eadr5912. doi: 10.1126/sciadv.adr5912.
Adaptive optics scanning light ophthalmoscopy (AOSLO) reveals individual retinal cells and their function, microvasculature, and micropathologies in vivo. As compared to the single-channel offset pinhole and two-channel split-detector nonconfocal AOSLO designs, by providing multidirectional imaging capabilities, a recent generation of multidetector and (multi-)offset aperture AOSLO modalities has been demonstrated to provide critical information about retinal microstructures. However, increasing detection channels requires expensive optical components and/or critically increases imaging time. To address this issue, we present an innovative combination of machine learning and optics as an integrated technology to compressively capture 12 nonconfocal channel AOSLO images simultaneously. Imaging of healthy participants and diseased subjects using the proposed deep compressed multichannel AOSLO showed enhanced visualization of rods, cones, and mural cells with over an order-of-magnitude improvement in imaging speed as compared to conventional offset aperture imaging. To facilitate the adaptation and integration with other in vivo microscopy systems, we made optical design, acquisition, and computational reconstruction codes open source.
自适应光学扫描光检眼镜（AOSLO）可在体内揭示单个视网膜细胞及其功能、微血管系统和微观病变。与单通道偏移针孔和双通道分离探测器非共焦AOSLO设计相比，新一代多探测器和（多）偏移孔径AOSLO模式通过提供多方向成像能力，已被证明能提供有关视网膜微观结构的关键信息。然而，增加检测通道需要昂贵的光学组件和/或显著增加成像时间。为解决这一问题，我们提出了一种机器学习与光学的创新组合，作为一种集成技术来同时压缩捕捉12幅非共焦通道AOSLO图像。使用所提出的深度压缩多通道AOSLO对健康参与者和患病受试者进行成像，结果显示与传统偏移孔径成像相比，杆状细胞、锥状细胞和壁细胞的可视化效果增强，成像速度提高了一个数量级以上。为便于与其他体内显微镜系统适配和集成，我们将光学设计、采集和计算重建代码开源。
