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.

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.

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