Toward drift-free high-throughput nanoscopy through adaptive intersection maximization

作者：Hongqiang Ma, Maomao Chen, Phuong Nguyen, Yang Liu · 发表于：Science Advances · 年份：2024 · DOI：10.1126/sciadv.adm7765 · 被引用次数：30 · 研究领域：Advanced Fluorescence Microscopy Techniques、Advanced Electron Microscopy Techniques and Applications、Photoacoustic and Ultrasonic Imaging

Single-molecule localization microscopy (SMLM) often suffers from suboptimal resolution due to imperfect drift correction. Existing marker-free drift correction algorithms often struggle to reliably track high-frequency drift and lack the computational efficiency to manage large, high-throughput localization datasets. We present an adaptive intersection maximization-based method (AIM) that leverages the entire dataset's information content to minimize drift correction errors, particularly addressing high-frequency drift, thereby enhancing the resolution of existing SMLM systems. We demonstrate that AIM can robustly and efficiently achieve an angstrom-level tracking precision for high-throughput SMLM datasets under various imaging conditions, resulting in an optimal resolution in simulated and biological experimental datasets. We offer AIM as one simple, model-free software for instant resolution enhancement with standard CPU devices.