The primary goal of this thesis is to improve the precision and efficiency of radiation therapy treatment. This goal is achieved by developing and implementing a direct aperture optimization (DAO) platform where the multileaf collimator (MLC) is rotated between each aperture. The approach is referred to as rotating aperture optimization (RAO). A series of tests is performed to evaluate how a final optimized plan depends on MLC parameters. Imposing constraints on the leaf sequence results in increased efficiency and a simplification of the treatment plan without compromising the quality of the dose distribution. It is also shown that an arrangement of equispaced collimator angles takes full advantage of the flexibility associated with collimator rotation. A study including ten recurring nasopharynx cancer patients is used to evaluate the capabilities of RAO compared to other optimization techniques. It is shown that RAO plans require significantly less linac radiation output (monitor units or MU) while maintaining equivalent dose distribution quality compared to plans generated with the conventional fluence based approach. Furthermore with an improved collimator rotation speed, the RAO plans should be executable in the same or less time than plans generated with the fluence-based approach. For the second part of the study it is shown that plans generated with RAO are as good as or better than plans generated with standard fixed collimator DAO. Film and ion chamber measurements indicate that RAO plans can be delivered more accurately than DAO plans. Additional applications of DAO were investigated through collaboration with two PhD students. First, Monte Carlo was used to generate pencil beam dose distributions for DAO inverse treatment planning (MC-DAO). The MC-DAO technique correctly models traditionally difficult treatment geometries such as small fields and tissue inhomogeneities. The MC-DAO also takes advantage of the improved MU efficiency associated with the DAO technique. Secondly DAO is proposed for adaptive radiation therapy. The results show that plan re-adaptation can be performed more quickly than complete plan regeneration thereby minimizing the time the patient has to spend in the treatment room and reducing the potential for geometric errors in treatment delivery.

The primary goal of this thesis is to improve the precision and efficiency of radiation therapy treatment. This goal is achieved by developing and implementing a direct aperture optimization (DAO) platform where the multileaf collimator (MLC) is rotated between each aperture. The approach is referred to as rotating aperture optimization (RAO). A series of tests is performed to evaluate how a final optimized plan depends on MLC parameters. Imposing constraints on the leaf sequence results in increased efficiency and a simplification of the treatment plan without compromising the quality of the dose distribution. It is also shown that an arrangement of equispaced collimator angles takes full advantage of the flexibility associated with collimator rotation. A study including ten recurring nasopharynx cancer patients is used to evaluate the capabilities of RAO compared to other optimization techniques. It is shown that RAO plans require significantly less linac radiation output (monitor units or MU) while maintaining equivalent dose distribution quality compared to plans generated with the conventional fluence based approach. Furthermore with an improved collimator rotation speed, the RAO plans should be executable in the same or less time than plans generated with the fluence-based approach. For the second part of the study it is shown that plans generated with RAO are as good as or better than plans generated with standard fixed collimator DAO. Film and ion chamber measurements indicate that RAO plans can be delivered more accurately than DAO plans. Additional applications of DAO were investigated through collaboration with two PhD students. First, Monte Carlo was used to generate pencil beam dose distributions for DAO inverse treatment planning (MC-DAO). The MC-DAO technique correctly models traditionally difficult treatment geometries such as small fields and tissue inhomogeneities. The MC-DAO also takes advantage of the improved MU efficiency associated with the DAO technique. Secondly DAO is proposed for adaptive radiation therapy. The results show that plan re-adaptation can be performed more quickly than complete plan regeneration thereby minimizing the time the patient has to spend in the treatment room and reducing the potential for geometric errors in treatment delivery.

The primary goal of this thesis is to improve the precision and efficiency of radiation therapy treatment. This goal is achieved by developing and implementing a direct aperture optimization (DAO) platform where the multileaf collimator (MLC) is rotated between each aperture. The approach is referred to as rotating aperture optimization (RAO). A series of tests is performed to evaluate how a final optimized plan depends on MLC parameters. Imposing constraints on the leaf sequence results in increased efficiency and a simplification of the treatment plan without compromising the quality of the dose distribution. It is also shown that an arrangement of equispaced collimator angles takes full advantage of the flexibility associated with collimator rotation. A study including ten recurring nasopharynx cancer patients is used to evaluate the capabilities of RAO compared to other optimization techniques. It is shown that RAO plans require significantly less linac radiation output (monitor units or MU) while maintaining equivalent dose distribution quality compared to plans generated with the conventional fluence based approach. Furthermore with an improved collimator rotation speed, the RAO plans should be executable in the same or less time than plans generated with the fluence-based approach. For the second part of the study it is shown that plans generated with RAO are as good as or better than plans generated with standard fixed collimator DAO. Film and ion chamber measurements indicate that RAO plans can be delivered more accurately than DAO plans. Additional applications of DAO were investigated through collaboration with two PhD students. First, Monte Carlo was used to generate pencil beam dose distributions for DAO inverse treatment planning (MC-DAO). The MC-DAO technique correctly models traditionally difficult treatment geometries such as small fields and tissue inhomogeneities. The MC-DAO also takes advantage of the improved MU efficiency associated with the DAO technique. Secondly DAO is proposed for adaptive radiation therapy. The results show that plan re-adaptation can be performed more quickly than complete plan regeneration thereby minimizing the time the patient has to spend in the treatment room and reducing the potential for geometric errors in treatment delivery. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

A previous approach to robust intensity-modulated radiation therapy (IMRT) treatment planning for moving tumours in the lung involves solving a single planning problem before treatment and using the resulting solution in all of the subsequent treatment sessions. In this thesis, we develop two adaptive robust IMRT optimization approaches for lung cancer, which involve using information gathered in prior treatment sessions to guide the reoptimization of the treatment for the next session. The first method is based on updating an estimate of the uncertain effect, while the second is based on additionally updating the dose requirements to account for prior errors in dose. We present computational results using real patient data for both methods and an asymptotic analysis for the first method. Through these results, we show that both methods lead to improvements in the final dose distribution over the traditional robust approach, but differ greatly in their daily dose performance.

A previous approach to robust intensity-modulated radiation therapy (IMRT) treatment planning for moving tumours in the lung involves solving a single planning problem before treatment and using the resulting solution in all of the subsequent treatment sessions. In this thesis, we develop two adaptive robust IMRT optimization approaches for lung cancer, which involve using information gathered in prior treatment sessions to guide the reoptimization of the treatment for the next session. The first method is based on updating an estimate of the uncertain effect, while the second is based on additionally updating the dose requirements to account for prior errors in dose. We present computational results using real patient data for both methods and an asymptotic analysis for the first method. Through these results, we show that both methods lead to improvements in the final dose distribution over the traditional robust approach, but differ greatly in their daily dose performance.

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京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第13032号 / 論医博第2114号 / 新制||医||1016(附属図書館) / 32990 / 横浜市立大学大学院医科学専攻 / (主査)教授 増永 慎一郎, 教授 坂井 義治, 教授 小川 修 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

The thesis aims to improve treatment dose accuracy in brachytherapy using a high dose rate (HDR) Ir-192 stepping source and in external beam therapy using intensity modulated radiation therapy (IMRT). For HDR brachytherapy, this has been achieved by investigating dose errors in the near field and the transit dose of the HDR brachytherapy stepping source. For IMRT, this study investigates the volume effect of detectors in the dosimetry of small fields, and the clinical implementation and dosimetric verification of a 6MV photon beam for IMRT. For the study of dose errors in the near field of an HDR brachytherapy stepping source, the dose rate at point P at 0.25 cm in water from the transverse bisector of a straight catheter was calculated with Monte Carlo code MCNP 4.A. The Monte Carlo (MC) results were used to compare with the results calculated with the Nucletron Brachytherapy Planning System (BPS) formalism. Using the MC calculated radial dose function and anisotropy function with the BPS formalism, 1% dose calculation accuracy can be achieved even in the near field with negligible extra demand on computation time. A video method was used to analyse the entrance, exit and the inter-dwell transit speed of the HDR stepping source for different path lengths and step sizes ranging from 2.5 mm to 995 mm. The transit speeds were found to be ranging from 54 to 467 mm/s. The results also show that the manufacturer has attempted to compensate for the effects of inter-dwell transit dose by reducing the actual dwell time of the source. A well-type chamber was used to determine the transit doses. Most of the measured dose differences between stationary and stationary plus inter-dwell source movement were within 2%. The small-field dosimetry study investigates the effect of detector size in the dosimetry of small fields and steep dose gradients with a particular emphasis on IMRT measurements. Due to the finite size of the detector, local discrepancies of more than 10 % are found between calculated cross profiles of intensity modulated beams and intensity modulated profiles measured with film. A method to correct for the spatial response of finite sized detectors and to obtain the

Geometric errors may compromise the quality of radiation therapy treatments. Optimization methods that account for errors can reduce their effects. The first paper of this thesis introduces minimax optimization to account for systematic range and setup errors in intensity-modulated proton therapy. The minimax method optimizes the worst case outcome of the errors within a given set. It is applied to three patient cases and shown to yield improved target coverage robustness and healthy structure sparing compared to conventional methods using margins, uniform beam doses, and density override. Information about the uncertainties enables the optimization to counterbalance the effects of errors. In the second paper, random setup errors of uncertain distribution---in addition to the systematic range and setup errors---are considered in a framework that enables scaling between expected value and minimax optimization. Experiments on a phantom show that the best and mean case tradeoffs between target coverage and critical structure sparing are similar between the methods of the framework, but that the worst case tradeoff improves with conservativeness. Minimax optimization only considers the worst case errors. When the planning criteria cannot be fulfilled for all errors, this may have an adverse effect on the plan quality. The third paper introduces a method for such cases that modifies the set of considered errors to maximize the probability of satisfying the planning criteria. For two cases treated with intensity-modulated photon and proton therapy, the method increased the number of satisfied criteria substantially. Grasping for a little less sometimes yields better plans. In the fourth paper, the theory for multicriteria optimization is extended to incorporate minimax optimization. Minimax optimization is shown to better exploit spatial information than objective-wise worst case optimization, which has previously been used for robust multicriteria optimization. The fifth and sixth papers introduce methods for improving treatment plans: one for deliverable Pareto surface navigation, which improves upon the Pareto set representations of previous methods; and one that minimizes healthy structure doses while constraining the doses of all structures not to deteriorate compared to a reference plan, thereby improving upon plans that have been reached with too weak planning goals. / <p>QC 20130516</p>

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23081号 / 医博第4708号 / 新制||医||1049(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 中本 裕士, 教授 小川 誠司, 教授 武田 俊一 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM