Abstract

Bioluminescent Imaging (BLI) is a widely utilised technique for the investigation of biological functions within preclinical biomedical studies. Its aim is to image distributed (biologically informative) visible and near infrared light sources, such as luciferase-tagged cells that are located within a living animal. Images are used to estimate the concentration and spatial distribution of reporters and therefore infer biological activity from measurements taken at the surface of the animal. Quantitative accuracy of the measurements can be improved by considering the highly attenuating and scattering nature of biological tissue, modelling the transport of the light through tissue to tomographically reconstruct a 3D image of the light source within the animal. This accuracy can be improved further by collecting spectral data of the bioluminescent signal. Compressive Sensing (CS) is a method of signal processing that utilises the sparse nature of real-world signals in order for them to be compressed in some domain. This in turn means that a sparse signal of length n can be represented by k<<n nonzero coefficients with high accuracy. Due to the localisation of bioluminescent sources, which are in sparse in nature, measurements can be collected using a CS based method. This work introduces the development of a CS based hyperspectral bioluminescent imaging system that can be used to collect compressed hyperspectral fluence data of an internal light source at the surface of an animal model. Effects of the number of measurements collected on image reconstruction quality are also investigated.

© 2019 SPIE/OSA

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