Compressive Fluorescence Microscopy for Biological and Hyperspectral Imaging

TL;DR

This paper demonstrates a compressive sensing fluorescence microscopy system that significantly reduces data acquisition rates while accurately imaging biological samples and hyperspectral data, highlighting its potential for efficient biomedical imaging.

Contribution

The paper presents a novel hardware implementation of compressive sensing in fluorescence microscopy, enabling high undersampling ratios and hyperspectral imaging capabilities.

Findings

Achieved image reconstructions with undersampling ratios up to 32 for fluorescent beads and tissues.

Demonstrated hyperspectral imaging with 128 spectral channels at an undersampling ratio of 64.

Showcased the potential of CS for high-dimensional, redundant biomedical signals.

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 out to some remaining challenges for CS fluorescence microscopy.