Centre for Optical and Electromagnetic Research, College of Optical Science and Engineering, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China; Taizhou Hospital, Zhejiang University, Taizhou 317000, China; Interdisciplinary Student Training Platform for Marine areas, Zhejiang University, Hangzhou 310027, China.
Centre for Optical and Electromagnetic Research, College of Optical Science and Engineering, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Feb 5;326:125178. doi: 10.1016/j.saa.2024.125178. Epub 2024 Sep 19.
Conventional microscopic spectral imaging suffers from extended scanning times across wavelength or spatial dimension. To improve capabilities of dynamic microscopic spectral imaging, we developed a snapshot computed tomographic microscopic imaging spectrometer (CTMIS) based on the zeroth and first orders dispersive diffraction of a two-dimensional grating. Utilizing the CTMIS-UNET reconstruction algorithm, we can reconstruct a spectral cube (541x541x26) for each frame of micro spectral imaging video. Experimental results demonstrate a sub-4 μm spatial resolution achievable through a 20x objective lens and a spectral resolution better than 10 nm among 450-700 nm, while maintaining spectral cosine similarities exceeding 0.9989 when comparing reconstructed spectra with ground truth data. Spectral imaging videos of four species of algae and mixed algae were captured under 10 ms exposure time using the CTMIS system. Leveraging the self-developed UNET-SI26 algae recognition network, precise identification and tracking of four types of algae and poisonous microcysts aeruginosa in mixed algae were conducted. The pixel-level recognition accuracy exceeds 95 %, while the accuracy for counting the numbers of different types of cells surpasses 85 %, offering an efficient and accurate spectral imaging method for real-time monitoring and early warning of harmful algae at the cellular level.
传统的微观光谱成像在波长或空间维度上的扫描时间较长。为了提高动态微观光谱成像的能力，我们开发了一种基于二维光栅零级和一级色散的快照计算层析显微成像光谱仪（CTMIS）。利用 CTMIS-UNET 重建算法，我们可以为每个微光谱成像视频的帧重建一个光谱立方体（541x541x26）。实验结果表明，通过 20x 物镜可以实现亚 4μm 的空间分辨率，在 450-700nm 范围内的光谱分辨率优于 10nm，同时在与真实数据重建光谱进行比较时保持光谱余弦相似度超过 0.9989。使用 CTMIS 系统，在 10ms 的曝光时间下，我们捕获了四种藻类和混合藻类的光谱成像视频。利用我们自主开发的 UNET-SI26 藻类识别网络，对四种藻类和混合藻类中的有毒微囊藻进行了精确的识别和跟踪。像素级别的识别准确率超过 95%，而对不同类型细胞数量的计数准确率超过 85%，为细胞水平的有害藻类实时监测和预警提供了一种高效、准确的光谱成像方法。
