Thesis (MScEng (Applied Mathematics))--University of Stellenbosch, 2006. / This thesis investigates the various components required for the development of a patient position verification system to replace the existing system used by the proton facilities of iThemba LABS1. The existing system is based on the visual comparison of a portal radiograph (PR) of the patient in the current treatment position and a digitally reconstructed radiograph (DRR) of the patient in the correct treatment position. This system is not only of limited accuracy, but labour intensive and time-consuming. Inaccuracies in patient position are detrimental to the effectiveness of proton therapy, and elongated treatment times add to patient trauma. A new system is needed that is accurate, fast, robust and automatic. Automatic verification is achieved by using image registration techniques to compare the PR and DRRs. The registration process finds a rigid body transformation which estimates the difference between the current position and the correct position by minimizing the measure which compares the two images. The image registration process therefore consists of four main components: the DRR, the PR, the measure for comparing the two images and the minimization method. The ray-tracing algorithm by Jacobs was implemented to generate the DRRs, with the option to use X-ray attenuation calibration curves and beam hardening correction curves to generate DRRs that approximate the PRs acquired with iThemba LABS’s digital portal radiographic system (DPRS) better. Investigations were performed mostly on simulated PRs generated from DRRs, but also on real PRs acquired with iThemba LABS’s DPRS. The use of the Correlation Coefficient (CC) and Mutual Information (MI) similarity measures to compare the two images was investigated. Similarity curves were constructed using simulated PRs to investigate how the various components of the registration process influence the performance. These included the use of the appropriate XACC and BHCC, the sizes of the DRRs and the PRs, the slice thickness of the CT data, the amount of noise contained by the PR and the focal spot size of the DPRS’s X-ray tube. It was found that the Mutual Information similarity measure used to compare 10242 pixel PRs with 2562 pixel DRRs interpolated to 10242 pixels performed the best. It was also found that the CT data with the smallest slice thickness available should be used. If only CT data with thick slices is available, the CT data should be interpolated to have thinner slices. Five minimization algorithms were implemented and investigated. It was found that the unit vector direction set minimization method can be used to register the simulated PRs robustly and very accurately in a respectable amount of time. Investigations with limited real PRs showed that the behaviour of the registration process is not significantly different than for simulated PRs.