High-density localization of multiple fluorescent emitters is a common strategy to improve the temporal
resolution of super-resolution localization microscopy. In recent years, various high-density localization
algorithms have been developed. Despite their rigorous mathematical model and the subsequent
improvement in image resolution, they still suffer from high computing complexity and the resulting
extremely low computation speed, thus limiting the application to either small dataset or expensive
computer clusters. It is still impractical as a routine tool for a large dataset. With the recent advance of
high-throughput localization microscopy with sCMOS cameras that can produce a huge amount of data
in a short period of time, fast processing now becomes even more important. Here, we present a simple
algebraic algorithm based on our previously developed method, gradient fitting, for fast and precise
high-density localization of multiple overlapping fluorescent emitters. Through numerical simulation and
biological experiments, we showed that our algorithm can yield comparable localization precision and
recall rate as DAOSTORM in various densities and signal levels, but with much simpler computation
complexity. After being implemented on a GPU device (NVidia GTX1080) for parallel computing, it can
run over three orders of magnitude faster than DAOSTORM implemented on a high-end workstation.
Therefore, our method presents a possibility for online reconstruction of high-speed super-resolution
imaging with high-density fluorescent emitters.
Hongqiang Ma and Yang Liu, "An ultra-fast algorithm for high-density localization microscopy (Conference Presentation)," Proc. SPIE 10350, Nanoimaging and Nanospectroscopy V, 103500G (Presented at SPIE Nanoscience + Engineering: August 06, 2017; Published: 29 September 2017); https://doi.org/10.1117/12.2275789.5593124849001.
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