Giga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusion

TL;DR

This paper presents a fast method for 4D fluorescence imaging that reconstructs giga-voxel data using minimal measurements by combining single-pixel cameras and data fusion, enabling detailed biomedical analysis.

Contribution

It introduces a novel system integrating two single-pixel cameras and a 2D detector with data fusion to efficiently reconstruct high-resolution 4D fluorescence data from limited measurements.

Findings

Reconstructed giga-voxel 4D hypercube with only 0.03% data

Achieved fast acquisition and processing of large fluorescence datasets

Enabled identification of fluorophore species via spectral and temporal signatures

Abstract

Time-resolved fluorescence imaging is a key tool in biomedical applications, as it allows to non-invasively obtain functional and structural information. However, the big amount of collected data introduces challenges in both acquisition speed and processing needs. Here, we introduce a novel technique that allows to reconstruct a Giga-voxel 4D hypercube in a fast manner while only measuring 0.03 % of the information. The system combines two single-pixel cameras and a conventional 2D array detector working in parallel. Data fusion techniques are introduced to combine the individual 2D and 3D projections acquired by each sensor in the final high-resolution 4D hypercube, which can be used to identify different fluorophore species by their spectral and temporal signatures.