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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/2273 |
| Date | 12 1900 |
| Creators | Van der Bijl, Leendert |
| Contributors | Muller, N., University of Stellenbosch. Faculty of Science. Dept. of Mathematical Sciences. Applied Mathematics. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Stellenbosch |
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