Year

2016

Degree Name

Doctor of Philosophy

Department

Centre for Medical Radiation Physics

Abstract

Positron emission tomography (PET) is a functional imaging technique used in clinical diagnostic applications and biomedical research. It is used as a non-invasive in vivo imaging modality to observe biochemical processes in small animal models, in particular for the study of diseases and to assist in the development of new treatments. A common approach to improving the spatial resolution of small animal PET scanners is to reduce the size of scintillation crystals and/or employ high resolution pixellated semiconductor detectors.

In this thesis, PETiPIX scanner is designed to achieve ultra high spatial resolution for imaging mice brains. Four Timepix pixellated silicon detector modules are placed in an edge-on configuration to form a scanner with a field of view (FoV) 15 mm in diameter. Each detector module consists of 256x256 pixels with dimensions of 55x55x300 µm3. Monte Carlo simulations using GEANT4 Application for Tomographic Emission (GATE) were performed to evaluate the feasibility of the PETiPIX design. Simulation results estimate a spatial resolution of 0.26 mm full width at half maximum (FWHM) at the centre of FoV and 0.29 mm FWHM overall spatial resolution with sensitivity of 0.01%.

With many of recent small animal PET scanner designs utilising high resolution pixellated semiconductor detectors, the large number of detector elements results in the system matrix - an essential part of statistical iterative reconstruction algorithms - becoming impractically large. A methodology is proposed in this thesis for system matrix modelling which utilises a virtual single-layer detector ring to greatly reduce the size of the system matrix without sacrificing precision. Two methods for populating the system matrix are compared; the first utilises a geometrically-derived system matrix based on Siddon's ray tracer method with the addition of an accurate detector response function, while the second uses Monte Carlo simulation to populate the system matrix. The effectiveness of both variations of the proposed technique is demonstrated via simulations of PETiPIX. Compression factors of 5x107 and 2:5x107 are achieved using this methodology for the system matrices produced using the geometric and Monte Carlo-based approaches, respectively, requiring a total of 0.5-1.2 GB of memory-resident storage. Images reconstructed from Monte Carlo simulations of various point source and phantom models, produced using system matrices generated via both geometric and simulation methods, are used to evaluate the quality of the resulting system matrix. The Monte Carlo-based system matrix is shown to provide the best image quality at the cost of substantial one-off computational effort and a lower compression factor. In addition, a straightforward extension of the virtual ring method to a three dimensional virtual cylinder is demonstrated using a 3D DoI PET scanner.

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