Positron emission tomography (PET) is a powerful and highly specialised imaging modality that has the inherent ability to detect and quantify changes in the bio-distribution of an intravenously administered radio-labelled tracer, through dynamic image acquisition of the system under study. By modelling the temporal distribution of the tracer, parameters of interest regarding specific biological processes can be derived. Traditionally parameter estimation is done by first reconstructing a set of dynamic images independently, followed by kinetic modelling, leading to parameters of reduced accuracy and precision. Furthermore only simple geometrical models are used during image reconstruction to model the mapping between the image space and the data space, leading to images of reduced resolution. This thesis attempts to address some of the problems associated with the current methodology, by implementing and evaluating new spatiotemporal image reconstruction strategies in oncology PET/CT imaging, with simulated, phantom and real data. More specifically this thesis is concerned with iterative reconstruction techniques, the incorporation of resolution recovery and kinetic modelling strategies within the image reconstruction process and the application of such methods in perfusion [15O]H2O imaging. This work is mainly based upon 2 whole body PET/CT scanners, the Siemens Biograph 6 B-HiRez and TruePoint TrueV, but some aspects of this work were also implemented for the High resolution research tomograph (HRRT).

abstract: Positron emission tomography (PET) is a non-invasive molecular imaging technique widely used for the quantification of physiological and biochemical processes in preclinical and clinical research. Due to its fundamental role in the health care system, there is a constant need for improvement and optimization of its scanner systems and protocols leading to a dedicated active area of research for PET. (Geant4 Application for Tomographic Emission (GATE) is a simulation platform designed to model and analyze a medical device. Monte Carlo simulations are essential tools to assist in optimizing the data acquisition protocols or in evaluating the correction methods for improved image quantification. Using GATE along with Customizable and Advanced Software for Tomographic Reconstruction (CASToR), provides a link to reconstruct the images. The goal of this thesis is to learn PET systems that involve Monte Carlo methods, GATE software, CASToR software to model, simulate and analyze PET systems using three clinical PET systems as a template. Fluorine-18 radioisotope source is used to perform measurements on the modeled PET systems. Parameters such as scatter-fraction, random-fraction, sensitivity, count rate performance, signal to noise ratio (SNR), and time of flight (ToF) are analyzed to determine the performance of the systems. Also, the simulated data are provided as input to CASToR software and Amide's a Medical Image Data Examiner (AMIDE) tool to obtain the reconstructed images which are used to analyze the reconstruction capability of the simulated models. The Biograph Vision PET model has high sensitivity (11.159 cps/MBq) and SNR (12.556) while the Ultra-High Resolution (UHR) PET model has high resolution of the reconstructed image. / Dissertation/Thesis / Masters Thesis Computer Engineering 2020

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