Spatio-temporal registration of dynamic PET data

Medical imaging plays an essential role in current clinical research and practice. Among the wealth of available imaging modalities, Positron Tomography Emission (PET) reveals functional processes in vivo by providing information on the interaction between a biological target and its tracer at the molecular level. A time series of PET images obtained from a dynamic scan depicts the spatio-temporal distribution of the PET tracer. Analysing the dynamic PET data then enables the quantification of the functional processes of interest for disease understanding and drug development. Given the time duration of a dynamic PET scan, which is usually 1-2 hours, any subject motion inevitably corrupts the tissue-tovoxel mapping during PET imaging, resulting in an unreliable analysis of the data for clinical decision making. Image registration has been applied to perform motion correction on misaligned dynamic PET frames, however, the current methods are solely based on spatial similarity. By ignoring the temporal changes due to PET tracer kinetics they can lead to inaccurate registration. In this thesis, a spatio-temporal registration framework of dynamic PET data is developed to overcome such limits. There are three scientific contributions made in this thesis. Firstly, the likelihood of dynamic PET data is formulated based on the generative model with both tracer kinetics and subject motion, providing a novel objective function. Secondly, the solution to the optimisation based on the generic plasma-input model is given, leading to the availability of a variety of biological targets. Thirdly, reference-input models are also incorporated to avoid blood sampling and thus extend the coverage of PET studies of the proposed framework. In the simulation-based validation, the proposed method achieves sub-voxel accuracy and its impact on clinical studies is evaluated on dopamine receptor data from an occupancy study, as well as breast cancer data from a reproducibility study. By successfully eliminating the motion artifacts as shown by visual inspection, the proposed method reduces the variability in clinical PET data and improves the confidence of deriving outcome measures on a study level. The motion correction algorithms developed in this thesis do not require any additional computational resources for a PET research centre, and they facilitate cost reduction by eliminating the need of acquiring extra PET scans in cases of motion corruption.