Radiation treatment planning is a process through which a certain plan is devised in order
to irradiate tumors or lesions to a prescribed dose without posing surrounding organs
to the risk of receiving radiation. A plan comprises a series of shots at di erent positions
with di erent shapes. The inverse planning approach which we propose utilizes certain
optimization techniques and builds mathematical models to come up with the right
location and shape, for each shot, automating the whole process. The models which
we developed for PerfexionTM unit (Elekta, Stockholm, Sweden), in essence, have come
to the assistance of oncologists in automatically locating isocentres and de ning sector
durations. Sector duration optimization (SDO) and sector duration and isocentre location
optimization (SDIO) are the two classes of these models. The SDO models, which
are, in fact, variations of equivalent uniform dose optimization model, are solved by two
nonlinear optimization techniques, namely Gradient Projection and our home-developed
Interior Point Constraint Generation. In order to solve SDIO model, a commercial optimization
solver has been employed. This study undertakes to solve the isocentre selection
and sector duration optimization. Moreover, inverse planning is evaluated, using clinical
data, throughout the study. The results show that automated inverse planning contributes
to the quality of radiation treatment planning in an unprecedentedly optimal
fashion, and signi cantly reduces computation time and treatment time.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/34010 |
| Date | 11 December 2012 |
| Creators | Ghaffari, Hamid |
| Contributors | Aleman, Dionne |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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