Computational based time-resolved multispectral fluorescence microscopy

Multispectral imaging and time-resolved imaging are two common acquisition schemes in fluorescence microscopy, and their combination can be beneficial to increase specificity. The multidimensionality of the dataset (space, time, and spectrum) introduces some challenges, such as the acquisition of big datasets and long measurement times. In this work, we present a time-resolved multispectral fluorescence microscopy system with a short measurement time, achieved by exploiting Compressive Sensing (CS) based on the Single-Pixel Camera (SPC) scheme. Data Fusion (DF) with a high-resolution camera allows us to tackle the problem of low spatial resolution, typical of SPC. The combined use of SPC, CS, and DF, in which hardware and algorithms are integrated, represents a computational imaging framework to reduce the number of measurements while preserving the information content. This approach has been exploited to demonstrate a zoom feature without moving the optical system. We describe and characterize the system in terms of spatial, spectral, and temporal properties, along with validation on a cellular sample.

Automated summary

In this work, we propose a time-resolved multispectral fluorescence microscopy system based on the Single-Pixel Camera (SPC) scheme, which achieves high resolution and short measurement time using Compressive Sensing (CS) technology. Data Fusion (DF) with a high-resolution camera is used to address the low spatial resolution issue typical of SPC. By integrating hardware and algorithms, this system reduces the number of measurements while preserving the information content, allowing for a zoom feature without moving the optical system. The system is characterized in terms of spatial, spectral, and temporal properties, and is validated on a cellular sample.