Hyperspectral fluorescence microscopy based on Compressive Sampling

TL;DR

This paper demonstrates a compressive sensing-based fluorescence microscopy system capable of high undersampling ratios, enabling efficient hyperspectral imaging of biomedical samples with reduced data acquisition rates.

Contribution

It presents a novel hardware implementation of compressive sensing in fluorescence microscopy, including hyperspectral imaging, with significant undersampling capabilities.

Findings

Achieved image reconstructions with undersampling ratios up to 32 for standard images.

Recorded hyperspectral images with 128 spectral channels at ratios up to 64.

Showcased potential for high-dimensional signal acquisition with reduced measurements.

Abstract

The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices-especially in optics-have only started being addressed. This paper presents an implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging. Our CS microscope combines a dynamic structured wide-field illumination and a fast and sensitive single-point fluorescence detection to enable reconstructions of images of fluorescent beads, cells, and tissues with undersampling ratios (between the number of pixels and number of measurements) up to 32. We further demonstrate a hyperspectral mode and record images with 128 spectral channels and undersampling ratios up to 64, illustrating the potential benefits of CS acquisition for higher-dimensional signals, which typically exhibits extreme redundancy. Altogether, our results emphasize the interest of CS schemes for acquisition at a significantly reduced rate and point to some remaining challenges for CS fluorescence microscopy.