Wavefront correction using machine learning methods for single molecule localization microscopy

Kayvan F. Tehrani; Jianquan Xu; Peter Kner

doi:10.1117/12.2077269

10 March 2015 Wavefront correction using machine learning methods for single molecule localization microscopy

Kayvan F. Tehrani, Jianquan Xu, Peter Kner

Proceedings Volume 9335, Adaptive Optics and Wavefront Control for Biological Systems; 93350L (2015) https://doi.org/10.1117/12.2077269
Event: SPIE BiOS, 2015, San Francisco, California, United States

Abstract

Optical Aberrations are a major challenge in imaging biological samples. In particular, in single molecule localization (SML) microscopy techniques (STORM, PALM, etc.) a high Strehl ratio point spread function (PSF) is necessary to achieve sub-diffraction resolution. Distortions in the PSF shape directly reduce the resolution of SML microscopy. The system aberrations caused by the imperfections in the optics and instruments can be compensated using Adaptive Optics (AO) techniques prior to imaging. However, aberrations caused by the biological sample, both static and dynamic, have to be dealt with in real time. A challenge for wavefront correction in SML microscopy is a robust optimization approach in the presence of noise because of the naturally high fluctuations in photon emission from single molecules. Here we demonstrate particle swarm optimization for real time correction of the wavefront using an intensity independent metric. We show that the particle swarm algorithm converges faster than the genetic algorithm for bright fluorophores.

Citation Download Citation

Kayvan F. Tehrani, Jianquan Xu, and Peter Kner "Wavefront correction using machine learning methods for single molecule localization microscopy", Proc. SPIE 9335, Adaptive Optics and Wavefront Control for Biological Systems, 93350L (10 March 2015); https://doi.org/10.1117/12.2077269

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