bioRxiv · 10.1101/659631
Divide and Conquer: Real-time maximum likelihood fitting of multiple emitters for super-resolution localization microscopy
Abstract
Multi-emitter localization has great potential for maximizing the imaging speed of super-resolution localization microscopy. However, the slow image analysis speed of reported multi-emitter localization algorithms limits their usage in mostly off-line image processing with small image size. Here we adopt the well-known divide and conquer strategy in computer science and present a fitting-based method called QC-STORM for fast multi-emitter localization. Using simulated and experimental data, we verify that QC-STORM is capable of providing real-time full image processing on raw images with 100 {micro}m x 100 {micro}m field of view and 10 ms exposure time, with comparable spatial resolution as the popular fitting-based ThunderSTORM and the up-to-date non-iterative WindSTORM. This study pushes the development and practical use of super-resolution localization microscopy in high-throughput or high-content imaging of cell-to-cell differences or discovering rare events in a large cell population.
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Li, L., Xin, B., Kuang, W., Zhou, Z., Huang, Z.-L.. 2019-06-04. Divide and Conquer: Real-time maximum likelihood fitting of multiple emitters for super-resolution localization microscopy. https://doi.org/10.1101/659631
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