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).