High SNR 3D Imaging from Millimeter-scale Thick Tissues to Cellular Dynamics via Structured Illumination Microscopy

TL;DR

This paper introduces a novel Hilbert-transform based decoding algorithm that significantly enhances SNR and imaging depth in structured illumination microscopy, enabling high-quality 3D imaging of thick tissues and live cells.

Contribution

The authors develop the HT-SHiLo algorithm, improving noise suppression and imaging depth in OS-SIM, facilitating detailed 3D imaging of complex biological tissues.

Findings

Significantly improved SNR in optical sectioning images.

Doubled imaging depth in thick tissues.

Achieved high-quality 3D images of diverse biological samples.

Abstract

Three-dimensional (3D) fluorescence imaging provides a vital approach for study of biological tissues with intricate structures, and optical sectioning structured illumination microscopy (OS-SIM) stands out for its high imaging speed, low phototoxicity and high spatial resolution. However, OS-SIM faces the problem of low signal-to-noise ratio (SNR) when using traditional decoding algorithms, especially in thick tissues. Here we propose a Hilbert-transform decoding and space domain based high-low (HT-SHiLo) algorithm for noise suppression in OS-SIM. We demonstrate HT-SHiLo algorithm can significantly improve the SNR of optical sectioning images at rapid processing speed, and double the imaging depth in thick tissues. With our OS-SIM system, we achieve high quality 3D images of various biological samples including mouse brains, Drosophila clock neurons, organoids, and live cells. We anticipate that this approach will render OS-SIM a powerful technique for research of cellular organelles or thick tissues in 3D morphology.