Single pixel hyperspectral bioluminescence tomography based on compressive sensing

Bentley, Alexander (2022). Single pixel hyperspectral bioluminescence tomography based on compressive sensing. University of Birmingham. Ph.D.

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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 can 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 outlines the development, testing and validation 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 acquisition 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-coloured sources demonstrating multi- wavelength light sources being recovered are presented further highlighting the benefits of the hyperspectral system for utilising multi-coloured biomarker applications.
Often it is the case where the optical parameters of the small animal are unknown leading to the use of a ‘best’ guess approach or to direct measurements using either a multi-modal or dedicated system. Using these conventional approaches, can lead to both inaccurate results and extending periods of imaging time. This work also introduces the development of an algorithm that is used to accurately localize the spatial light distribution from a bioluminescence source within a subject by simultaneously reconstructing both the underlying optical properties and source spatial distribution and intensity from the same set of surface measurements. Through its application in 2- and 3-dimensional, homogeneous and heterogenous numerical models, it is demonstrated that the proposed algorithm is capable of replicating results as compared to ‘gold’ standard where the absolute optical properties are known. Additionally, the algorithm has been applied to experimental data using a tissue mimicking block phantom and real bioluminescent mice models, recovering spatial light distributions that have localization errors of ~ 1 mm and intensities which are similar if not better than results gained using ‘gold’ standard methods without the need of assumptions regarding the underlying optical properties or source distribution.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):