AI 中文总结
该研究开发了AI驱动的柔性光纤束内窥镜平台，通过光学-计算协同设计及GAME流水线提升NIR-II成像分辨率，实现体内生物样本高分辨率成像，为临床转化奠定基础。
AI 中文摘要
光纤束内窥镜为通过人体自然腔道开展临床荧光成像提供了紧凑且柔性的途径，但自20世纪50年代首次被报道以来，它一直受限于空间分辨率低、蜂窝状伪影以及纤芯间串扰的问题。在近红外二区（NIR-II，1000-3000nm）波长下，串扰会更为显著，而NIR-II是生物医学成像中具备优异对比度、分辨率和组织穿透深度的光谱窗口。在此，我们提出了一种AI驱动的柔性内窥镜平台，该平台通过光学-计算协同设计克服了上述局限：优化超薄光纤束以减轻串扰引发的图像模糊，实现从可见光到NIR-II光谱范围的高保真图像传输；并开发了智能体引导的混合专家（GAME）流水线，用于去除蜂窝状伪影和图像恢复。GAME为该内窥镜采集的各类生物医学图像（涵盖细胞、小鼠及人类样本）提供了单一恢复入口，它通过视觉-语言模型将每个输入动态路由至合适的恢复专家，实现了超越奈奎斯特-香农采样极限四倍的分辨率提升。我们的内窥镜的实用性通过小鼠体内NIR-II解剖结构成像，以及数字微镜器件（DMD）投影的人类胃管和淋巴系统成像得到验证，为未来的临床转化铺平了道路。
英文摘要
Fiber-bundle endoscopy offers a compact and flexible route for clinical fluorescence imaging through natural human orifices, but since its first report in the 1950s, it has remained limited by low spatial resolution, honeycomb artifacts, and inter-core crosstalk. The crosstalk becomes more pronounced at near-infrared-II wavelengths (NIR-II, 1000-3000 nm), a spectral window that offers superior contrast, resolution, and tissue penetration depth for biomedical imaging. Here, we present an AI-powered flexible endoscopy platform that overcomes these constraints through optical-computational co-design: optimizing ultrathin fiber bundles to mitigate crosstalk-induced image blur and enable high-fidelity image transmission across the visible-to-NIR-II spectral range, and developing an Agent-Guided Mixture-of-Experts (GAME) pipeline for honeycomb-artifact removal and image restoration. GAME provides a single restoration entry point for diverse biomedical images acquired with our endoscope, spanning cell, mouse and human samples. It dynamically routes each input to suitable restoration experts via a vision-language model, facilitating image reconstruction with a fourfold resolution improvement beyond the NyquistShannon sampling limit. The utility of our endoscope is demonstrated through in vivo NIR-II imaging of anatomical structures in mice, as well as imaging of the digital micromirror device (DMD)-projected human gastric tube and lymphatic system, paving the way for future clinical translation.
