Recent advances in point spread function engineering and related computational microscopy approaches: from one viewpoint
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REVIEW
Recent advances in point spread function engineering and related computational microscopy approaches: from one viewpoint Yoav Shechtman 1 Accepted: 5 November 2020 # International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This personal hybrid review piece, written in light of my recipience of the UIPAB 2020 young investigator award, contains a mixture of my scientific biography and work so far. This paper is not intended to be a comprehensive review, but only to highlight my contributions to computation-related aspects of super-resolution microscopy, as well as their origins and future directions. Keywords Super resolution microscopy . Point-spread function engineering . Single particle tracking . Deep learning
The past In 2008–2013, I was a graduate student at the Technion – Israel Institute of Technology. My research, under the supervision of Prof. Mordechai (Moti) Segev in collaboration with Prof. Yonina Eldar, dealt with recovering seemingly lost information in optical signals, such as phase and high spatial frequencies, by using the prior knowledge of signal sparsity (Shechtman et al. 2010; 2014a, b; Szameit et al. 2012). Towards the end of my studies, I took a class in 3D imaging, where I learned something fascinating, that would change the trajectory of my career: the point spread function (PSF) of an imaging system can be manipulated wildly, such that a point source no longer appears as a point at all—but rather as two points, with an orientation that depends on the distance of the source from the imaging system (Pavani et al. 2009) (Fig. 1). I was immediately hooked by this elegant method to encode depth information in a 2D image, and the simplicity of its implementation: placing a phase mask in the back focal plane of the imaging system. I asked the professor, Yoav Schechner, whether this PSF was the optimal shape to encode 3D information in a 2D image, not knowing at the time that this question would occupy my mind for the next few years.
* Yoav Shechtman [email protected] 1
Department of Biomedical Engineering and Lorry Lokey Interdisciplinary Center for Life Sciences and Engineering, Technion–Israel Institute of Technology, 3200003 Haifa, Israel
After my PhD, I decided to take a step towards applying my optics and engineering background to biological problems, very broadly defined. I was fortunate enough to find a perfect place for this ambition: W.E. Moerner’s group at Stanford University. At Stanford, I started working on a beautiful system for single-molecule measurements based on electrokinetic trapping, known as the Anti-Brownian ELectrokinetic trap (ABEL) trap (Cohen and Moerner 2005; Squires et al. 2018; Wang and Moerner 2014). This is a system that enables observation of single molecule kinetics in solution over extended durations, by exerting force that negates their Brownian motion, keeping them trapped inside a small observation region. My plan was to apply compressed sensing (Candès 2006; Donoho
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