Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy
Alessandro Zunino, Giacomo Garr\`e, Eleonora Perego, Sabrina Zappone,, Mattia Donato, Giuseppe Vicidomini

TL;DR
This paper introduces a novel structured detection method for laser scanning microscopy that achieves super-resolution, optical sectioning, and high SNR simultaneously, while also enabling super-sampling and fluorescence lifetime imaging.
Contribution
The authors propose a new reconstruction algorithm based on the physical model of ISM that overcomes current limitations, validated with synthetic and real biological samples.
Findings
Achieves super-resolution, optical sectioning, and high SNR simultaneously.
Enables super-sampling, relaxing Nyquist's criterion by a factor of two.
Generalizes to fluorescence lifetime imaging with single-photon timing.
Abstract
Fast and sensitive detector arrays enable image scanning microscopy (ISM), overcoming the trade-off between spatial resolution and signal-to-noise ratio (SNR) typical of confocal microscopy. However, current ISM approaches cannot provide optical sectioning and fail with thick samples, unless the size of the detector is limited. Thus, another trade-off between optical sectioning and SNR persists. Here, we propose a method without drawbacks that combines uncompromised super-resolution, high SNR, and optical sectioning. Furthermore, our approach enables super-sampling of images, relaxing Nyquist's criterion by a factor of two. Based on the observation that imaging with a detector array inherently embeds axial information about the sample, we designed a straightforward reconstruction algorithm that inverts the physical model of ISM. We present the comprehensive theoretical framework and…
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Taxonomy
TopicsImage Processing Techniques and Applications · Optical measurement and interference techniques · Advanced Optical Sensing Technologies
