用AI让普通荧光显微镜实现超薄层成像，突破传统技术深度限制。
Computational TIRF enables optical sectioning beyond the evanescent field for widefield fluorescence microscopy
- 基于深度学习与物理模型融合，从常规荧光图像重建出类TIRF效果
- 在密集标记样本中实现单帧和体积层面的清晰断层成像
- 无需硬件改动，快速适配新设备，适合生物医学影像研究者
宽场荧光显微镜的分辨能力因离焦背景而受限，尤其在高密度标记的生物样本中更为明显。尽管全内反射荧光（TIRF）显微镜能提供强近表面光学切片，但其成像深度有限。本文提出计算型TIRF（cTIRF），一种基于深度学习的成像方法，仅需常规宽场激发荧光测量数据即可生成类TIRF的截面图像，无需任何光学改造。通过将物理驱动的前向模型融入网络训练，cTIRF实现了有效背景抑制与轴向分辨率提升，同时保持与实测宽场数据一致。实验表明，cTIRF在近表面结构恢复上表现接近真实TIRF，且在传统TIRF无法工作的密集标记样本中，成功实现单帧及三维体数据的截面重建。该工作确立了cTIRF作为硬件光学切片的实际替代方案，可快速适配新成像系统，仅需少量校准数据。
原文摘要 · Abstract (English)
The resolving ability of widefield fluorescence microscopy is fundamentally limited by out-of-focus background owing to its low axial resolution, particularly for densely labeled biological samples. Although total internal reflection fluorescence (TIRF) microscopy provides strong near-surface sectioning, they are intrinsically restricted to shallow imaging depths. Here we present computational TIRF (cTIRF), a deep learning-based imaging modality that generates TIRF-like sectioned images directly from conventional widefield epifluorescence measurements without any optical modification. By integrating a physics-informed forward model into network training, cTIRF achieves effective background suppression and axial resolution enhancement while maintaining consistency with the measured widefield data. We demonstrate that cTIRF recovers near-surface structures with performance comparable to experimental TIRF, and further enables both single-frame and volumetric sectioned reconstruction in densely labeled samples where conventional TIRF fails. This work establishes cTIRF as a practical and deployable alternative to hardware-based optical sectioning in fluorescence microscopy, enabled by rapid adaptation to new imaging systems with minimal calibration data.
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