The State University of New York at Buffalo, Department of Electrical Engineering, Buffalo, New York, United States.
Beijing Institute of Technology, School of Information and Electronics, Beijing, China.
J Biomed Opt. 2021 Feb;26(2). doi: 10.1117/1.JBO.26.2.026501.
Single-molecule localization-based super-resolution microscopy has enabled the imaging of microscopic objects beyond the diffraction limit. However, this technique is limited by the requirements of imaging an extremely large number of frames of biological samples to generate a super-resolution image, thus requiring a longer acquisition time. Additionally, the processing of such a large image sequence leads to longer data processing time. Therefore, accelerating image acquisition and processing in single-molecule localization microscopy (SMLM) has been of perennial interest.
To accelerate three-dimensional (3D) SMLM imaging by leveraging a computational approach without compromising the resolution.
We used blind sparse inpainting to reconstruct high-density 3D images from low-density ones. The low-density images are generated using much fewer frames than usually needed, thus requiring a shorter acquisition and processing time. Therefore, our technique will accelerate 3D SMLM without changing the existing standard SMLM hardware system and labeling protocol.
The performance of the blind sparse inpainting was evaluated on both simulation and experimental datasets. Superior reconstruction results of 3D SMLM images using up to 10-fold fewer frames in simulation and up to 50-fold fewer frames in experimental data were achieved.
We demonstrate the feasibility of fast 3D SMLM imaging leveraging a computational approach to reduce the number of acquired frames. We anticipate our technique will enable future real-time live-cell 3D imaging to investigate complex nanoscopic biological structures and their functions.
基于单分子定位的超分辨率显微镜使我们能够对超越衍射极限的微观物体进行成像。然而,这种技术受到需要对生物样本的大量帧进行成像以生成超分辨率图像的要求的限制,因此需要更长的采集时间。此外,处理如此大的图像序列会导致更长的数据处理时间。因此,加速单分子定位显微镜(SMLM)中的图像采集和处理一直是人们长期关注的问题。
通过利用计算方法加速三维(3D)SMLM 成像,而不会影响分辨率。
我们使用盲稀疏补全技术从低密度图像重建高密度 3D 图像。低密度图像的生成使用的帧数比通常需要的少得多,因此需要更短的采集和处理时间。因此,我们的技术将在不改变现有的 SMLM 硬件系统和标记协议的情况下加速 3D SMLM。
盲稀疏补全的性能在模拟和实验数据集上进行了评估。在模拟中,使用多达 10 倍的帧数和在实验数据中使用多达 50 倍的帧数即可实现 3D SMLM 图像的优越重建结果。
我们证明了利用计算方法减少采集帧数来实现快速 3D SMLM 成像的可行性。我们预计,我们的技术将能够实现未来实时活细胞 3D 成像,以研究复杂的纳米级生物结构及其功能。