快速宽场三维显微技术研究进展

Advances in Rapid Three-Dimensional Wide Field Microscopy

激光扫描显微镜通过扫描高度汇聚的激光焦点可以获得样品的三维图像,而激光扫描显微镜时间分辨率低、光毒性大的缺点限制了其在活体快速三维成像等领域中的应用。近年来具有三维成像能力的宽场显微镜技术逐渐成为三维成像领域的研究热点。聚焦形貌恢复技术、结构光照明显微技术以及深度学习辅助三维成像是三种基于宽场成像的快速三维成像技术,通过硬件提升和软件辅助的方式,提高了宽场显微镜的三维成像能力。分别介绍了它们的原理、优缺点、最新的研究进展与应用,最后对宽场三维显微技术的未来发展进行了总结与展望。

Significance Threedimensional(3D)imaging is an important research direction in microscopy and has been applied to many fields such as biomedicine and engineering science.Typical 3D microscopy techniques,such as laser confocal microscopy and multiphoton microscopy,are based on laser point scanning geometry,and the imaging speed is limited by the scanning speed;therefore,biological samples are likely to be damaged under long scanning durations and highintensity illumination.Recently,widefield microscopy with 3D imaging capability has received significant attention.Widefield microscopy can yield complete twodimensional imaging simultaneously and affords temporal resolutions higher than spot scanning by two to three orders of magnitude.Additionally,widefield imaging offers highquality grayscale images and fewer samples to be damaged,thus rendering it suitable for the realtime observation of living samples.However,conventional widefield microscopy suffers from defocused backgrounds and low axial resolutions.Owing to the rapid development of computer science and optical technology,various algorithms and techniques for processing widefield images have been proposed to improve their axial resolutions,thus providing more possibilities for 3D imaging.We focus on three types of rapid 3D widefield microscopy techniques,i.e.,shape of focus(SFF),structural illumination microscopy(SIM),and deep learningassisted 3D imaging.The SFF technique enables the extraction of focal plane information by processing a series of image stacks and reconstructing the 3D morphology of samples without requiring specific hardware.In SIM,samples are illuminated by phaseshifted light fields with high spatial frequencies,images are captured using a CCD camera,and the in-and outfocus information can be effectively separated using decoding algorithms.Deep learning models can learn the mapping relationship between different types of images from a large amount of data,such as the conversion between widefield images and confocal images;this is a simple method to obtain highquality images.The trained model can remove the background information of widefield microscopic images to improve the axial resolution of imaging,thus facilitating the realization of 3D imaging via widefield microscopy.It is believed that rapid 3D microscopes based on widefield imaging will be applied to many fields such as biomedicine,materials science,and precision manufacturing in the near future.Progress This paper focuses on three rapid widefield 3D imaging techniques,namely,SFF,SIM,and deep learningassisted 3D imaging.In the SFF technique,a focusing evaluation operator is used to calculate and extract the highest focus position of each pixel from a widefield image stack;subsequently,the 3D depth image of the sample is reconstructed via a recovery algorithm,which is mainly used for surface topography measurement.We investigate the effect of the focused evaluation operator on the calculation results yielded by the SFF technique in different cases.Additionally,we discuss the development and application of the focused topography recovery operator and the optimization of related hardware.Optical sectioning SIM utilizes encoded structured light fields to illuminate the sample and then recovers the 3D information of the sample using decoding algorithms,which can be used for both fluorescent and nonfluorescent imaging.We introduce the theoretical basis of optical sectioning SIM and then propose various rapid decoding algorithms for improving the reconstruction speed.Then,we discuss the development of related techniques and their most recent applications in the field of 3D color imaging.Deep learningassisted 3D imaging applies the learning ability of neural network models to complete target image tasks,such as the conversion between widefield and confocal images as well as that between widefield microscopy and SIM so as to achieve widefield 3D imaging.We present the theoretical basis of deep learningrelated models.Subsequently,we discuss the development and application of deep learning models for conversion between widefield and confocal images as well as that between widefield microscopy and SIM,followed by the applications of deep learning for achieving more rapid SIM imaging.Finally,we discuss the current problems and future research directions for rapid 3D widefield microscopy techniques.Conclusions and Prospects Rapid 3D widefield microscopy techniques have demonstrated performance improvement either through hardware modification or software assistance.However,these techniques are not perfect.SFF combined with other techniques is expected to benefit deep tissue imaging.The amount of SIM imaging data is two to three times that of the conventional widefield microscopy,and the imaging speed of SIM can be further improved.Deep learning can be flexibly combined with other technologies.In summary,the potential of widefield microscopy with 3D imaging capability is yet to be realized.Progress in technology and cross integration will enable the routine use of rapid 3D widefield microscopy techniques in biomedical laboratories.