Single pixel hyperspectral bioluminescence tomography based on compressive sensing

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

Abstract

Photonics based imaging is a widely utilised technique for the study of biological functions within pre-clinical studies. Specifically, Bioluminescence Imaging is a sensitive non-invasive and non-contact optical imaging technique that is able to detect distributed (biologically informative) visible and near-infrared activated light sources within tissue, providing information about tissue function. Compressive Sensing (CS) is a method of signal processing that works on the basis that a signal or image can be compressed without important information being lost. This work describes the development of a CS based hyperspectral Bioluminescence imaging system that is used to collect compressed fluence data from the external surface of an animal model, due to an internal source, providing lower acquisition times, higher spectral content and potentially better tomographic source localisation. The work demonstrates that hyperspectral surface fluence images of both block and mouse shaped phantom due to internal light sources could be obtained at 30% of the time and measurements it would take to collect the data using conventional raster scanning methods. Using hyperspectral data, tomographic reconstruction of internal light sources can be carried out using any desired number of wavelengths and spectral bandwidth. Reconstructed images of internal light sources using four wavelengths as obtained through CS are presented showing a localisation error of ~3 mm. Additionally, tomographic images of dual-colored sources demonstrating multi-wavelength light sources being recovered are presented further highlighting the benefits of the hyperspectral system for utilising multi-colored biomarker applications.

Details

Original languageEnglish
Pages (from-to)5549-5564
Number of pages16
JournalBiomedical Optics Express
Volume10
Issue number11
Publication statusPublished - 7 Oct 2019