Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy

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 validate our method with synthetic and experimental images of biological samples captured using a custom setup equipped with a single-photon avalanche diode (SPAD) array detector. We demonstrate the feasibility of our approach exciting fluorescence emission both in the linear and non-linear regime. Moreover, we generalize the algorithm for fluorescence lifetime imaging, fully exploiting the single-photon timing ability of the SPAD array detector. Our method outperforms conventional approaches to ISM and can be extended to any LSM technique.