Abstract
PET image degradation imposed by patient respiratory motion is a well-established problem in clinical oncology; strategies exist to study and correct this. Some attempt to minimise or arrest patient motion through restraining hardware; their effectiveness is subject to the comfort and compliance. Another practice is to gate PET data based on signals acquired from an external device. This thesis presents several contributions to the field of respiratory motion correction research in PET imaging. First and foremost, this thesis presents a framework which allows a researcher to process list mode data from a Siemens Biograph mCT scanner and reconstruct sinograms of which in the open source image reconstruction package STIR. Secondly, it demonstrates the viability of a depth camera for respiratory monitoring and gating in a clinical environment. It was demonstrated that it was an effective device to capture anterior surface motion. Similarly, it has been shown that it can be used to perform respiratory gating. The third contribution is the design, implementation and validation of a novel respiring phantom. It has individually programmable degrees of freedom and was able to reproduce realistic respiration motion derived from real volunteers. The final contribution is a new gating algorithm which optimises the number and width of gates based on respiratory motion data and the distribution of radioactive counts. This new gating algorithm iterates on amplitude based gating, where gates as positioned based on respiratory pose at a given instant. The key improvement is that it considers the distribution of counts as a consequence of the distribution of motion in a typical PET study. The results show that different studies can be optimised with a unique number of gates based on the maximum extent of motion present and can take into account shifts in baseline position due to patient perturbation. 7