https://doi.org/10.1051/epjconf/202430904021
s2ISM: A Comprehensive Approach for Uncompromised Super-Resolution and Optical Sectioning in Image Scanning Microscopy
1 Istituto Italiano di Tecnologia, Molecular Microscopy and Spectroscopy, Genova, Italy
2 Università degli sludi di Genova, DIBRIS, Genova, Italy
* Corresponding author: alessandro.zunino@iit.it
Published online: 31 October 2024
Image Scanning Microscopy (ISM) enables good signal-to-noise ratio (SNR), super-resolution and high information content imaging by leveraging array detection in a laser-scanning architecture. However, the SNR is still limited by the size of the detector, which is conventionally small to avoid collecting out-of-focus light. Nonetheless, the ISM dataset inherently contains the axial information of the fluorescence emitters. We leverage this knowledge to achieve computational optical sectioning without sacrificing the conventional benefits of ISM. We invert the physical model to fuse the raw dataset into a single image with improved sampling, SNR. lateral resolution, and optical sectioning. We provide a complete theoretical framework and validate our approach with experimental images of biological samples acquired with a custom setup equipped with a single photon avalanche diode (SPAD) array detector. Furthermore, we generalize our method to other imaging techniques, such as multi-photon excitation fluorescence microscopy and fluoresce lifetime imaging. To enable this latter, we take advantage of the single-photon timing ability of SPAD arrays, accessing additional sample information. Our method outperforms conventional reconstruction techniques and opens new perspectives for exploring the unique spatio-temporal information provided by SPAD array detectors.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
