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A dual-field strategy is often used for tumors with highly complex shapes and/or with large volumes exceeding available field-size in both passive and scanning irradiations with ion beams. Range and setup uncertainties can cause hot and cold doses at the field junction within the target. Such uncertainties will also cause cold doses in the peripheral region of the target. We have developed an algorithm to reduce the sensitivity of the dual-field plan to these uncertainties in scanning irradiations. This algorithm is composed of the following two steps: 1) generating the expanded target volume, and 2) solving the inverse problem where the terms suppressing the dose gradient of individual fields are added into the objective function. The validity of this algorithm is demonstrated through the simulation studies for three extreme cases of two fields with unidirectional, opposing and orthogonal geometries. With the proposed algorithm, we can obtain a more robust plan to minimize the effects of range and setup uncertainties than the conventional plan. Compared to that for the conventional plan, the optimization time for the robust plan increased by a factor of approximately three.
An extensive comparison of the path uncertainty in single particle tracking systems for ion imaging was carried out based on Monte Carlo simulations. The spatial resolution as function of system parameters such as geometry, detector properties and th
Purpose: Beam range control is the essence of radiotherapy with heavy charged particles. In conventional broad-beam delivery, fine range adjustment is achieved by insertion of range shifting and compensating materials. In dosimetry, solid phantoms ar
In dual-energy computed tomography (DECT), low- and high- kVp data are collected often over a full-angular range (FAR) of $360^circ$. While there exists strong interest in DECT with low- and high-kVp data acquired over limited-angular ranges (LARs),
In this paper, we solve the multiple product price optimization problem under interval uncertainties of the price sensitivity parameters in the demand function. The objective of the price optimization problem is to maximize the overall revenue of the
The parallel machine scheduling problem has been a popular topic for many years due to its theoretical and practical importance. This paper addresses the robust makespan optimization problem on unrelated parallel machine scheduling with sequence-depe