基于多层聚焦的三维结构光照明显微成像模型与重建算法研究

细胞是生命的基本单元。细胞内有众多细胞器，其结构和功能的紊乱会导致各种疾病。结构光照明荧光显微镜利用摩尔条纹效应实现了三维超分辨率成像，能观察到更清晰的细胞器。但三维超分辨率成像需要采集更多的原始图像和光子数，其空间分辨率的提升是以牺牲时间分辨率和光子利用率为代价的。在荧光总光子数有限的前提下，光子利用率降低是导致成像总时程变短的重要原因。所以现阶段的三维结构光照明显微镜仍存在时间分辨率低、成像总时程短及重建伪影多等问题，很难适用于活细胞快速、长时程成像。本项目拟构建多层聚焦三维结构光照明成像模型，配合光学系统，能提高成像的时间分辨率；同时基于成像模型和先验信息，提出了预平均纯相位参数估计方法和时空连续性正则的迭代重建算法，允许三维结构光照明显微镜采集的原始图像光子数减少、信噪比降低时，仍能准确估计出照明光参数并抑制重建图像中的伪影，最终在活细胞上实现快速、长时程三维超分辨成像。

Cells are the basic unit of life and contain numerous organelles. The disruption of organelles’ structure and function will lead to many diseases. Three-dimensional structured illumination fluorescence microscopy (3D-SIM) improves the resolution with Moire fringe and can observe clearer organelle in living cells. However, the 3D-SIM requires more raw images and a higher photon count to reconstruct one volume of three-dimensional super-resolution images, leading to a low temporal resolution and photon efficiency. Under the condition that the fluorescence photon is limited, the reduced photon efficiency is an important reason for the short imaging duration. Therefore, the state-of-the-art 3D-SIM cannot achieve the high temporal resolution, long-term imaging duration and artifacts-free reconstruction in super-resolution imaging of living cells. In this project, a multiplane 3D-SIM was proposed to achieve a higher temporal resolution. Meanwhile, a pre-averaged and amplitude-normalized parameter estimation algorithm was proposed to derive the parameters of structured patterns from the low signal-to-noise ratio raw images. Besides, a regularization reconstruction algorithm based on spatiotemporal continuity was proposed to suppress the artifacts in the reconstructed three-dimensional super-resolution images. The above improvements can allow 3D-SIM to be used for fast, long-term, three-dimensional super-resolution imaging of living cells.