IMRT treatment plans for step-and-shoot delivery have traditionally been produced through the optimization of intensity distributions (or maps) for each beam angle. The optimization step is followed by the application of a leaf-sequencing algorithm that translates each intensity map into a set of deliverable aperture shapes. In this article, we introduce an automated planning system in which we bypass the traditional intensity optimization, and instead directly optimize the shapes and the weights of the apertures. We call this approach "direct aperture optimization." This technique allows the user to specify the maximum number of apertures per beam direction, and hence provides significant control over the complexity of the treatment delivery. This is possible because the machine dependent delivery constraints imposed by the MLC are enforced within the aperture optimization algorithm rather than in a separate leaf-sequencing step. The leaf settings and the aperture intensities are optimized simultaneously using a simulated annealing algorithm. We have tested direct aperture optimization on a variety of patient cases using the EGS4/BEAM Monte Carlo package for our dose calculation engine. The results demonstrate that direct aperture optimization can produce highly conformal step-and-shoot treatment plans using only three to five apertures per beam direction. As compared with traditional optimization strategies, our studies demonstrate that direct aperture optimization can result in a significant reduction in both the number of beam segments and the number of monitor units. Direct aperture optimization therefore produces highly efficient treatment deliveries that maintain the full dosimetric benefits of IMRT.

Both VMAT and HT plans can be delivered accurately based on their own QA standards. Overall, VMAT was able to provide approximately a 40% reduction in treatment time while maintaining comparable plan quality to that of HT.

Abstract. In the field of radiation therapy, much of the research is aimed at developing new and innovative techniques for treating cancer patients with radiation. In recent years, new treatment machines have been developed that provide a much greater degree of computer control than was available with the machines of previous generations. One innovation has been the development of an approach called "tomotherapy." Tomotherapy can be defined as computer-controlled rotational radiotherapy delivered using an intensity-modulated fan beam of radiation. The successful implementation of the new delivery techniques requires the development of a suitable approach for optimizing each patient's treatment plan. One of the challenges is to quantify optimality in radiation therapy. We have tested a variety of objective functions and constraints in pursuit of a formulation that performs well for a wide variety of disease sites. An additional challenge stems from the sizable amount of data and the large number of variables that are involved in each optimization. This paper presents several approaches to optimizing treatment plans in radiation therapy, and the advantages and disadvantages of a number of formulations are explored.

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Intensity-modulated arc therapy (IMAT) is a radiation therapy delivery technique that combines gantry rotation with dynamic multi-leaf collimation (MLC). With IMAT, the benefits of rotational IMRT can be realized using a conventional linear accelerator and a conventional MLC. Thus far, the advantages of IMAT have gone largely unrealized due to the lack of robust automated planning tools capable of producing efficient IMAT treatment plans. This work describes an inverse treatment planning algorithm, called 'direct aperture optimization' (DAO) that can be used to generate inverse treatment plans for IMAT. In contrast to traditional inverse planning techniques where the relative weights of a series of pencil beams are optimized, DAO optimizes the leaf positions and weights of the apertures in the plan. This technique allows any delivery constraints to be enforced during the optimization, eliminating the need for a leaf-sequencing step. It is this feature that enables DAO to easily create inverse plans for IMAT. To illustrate the feasibility of DAO applied to IMAT, several cases are presented, including a cylindrical phantom, a head and neck patient and a prostate patient.