Algorithm and performance of a clinical IMRT beam-angle optimization system

This paper describes the algorithm and examines the performance of an IMRT beam-angle optimization (BAO) system. In this algorithm successive sets of beam angles are selected from a set of predefined directions using a fast simulated annealing (FSA) algorithm. An IMRT beam-profile optimization is performed on each generated set of beams. The IMRT optimization is accelerated by using a fast dose calculation method that utilizes a precomputed dose kernel. A compact kernel is constructed for each of the predefined beams prior to starting the FSA algorithm. The IMRT optimizations during the BAO are then performed using these kernels in a fast dose calculation engine. This technique allows the IMRT optimization to be performed more than two orders of magnitude faster than a similar optimization that uses a convolution dose calculation engine.Comment: Final version that appeared in Phys. Med. Biol. 48 (2003) 3191-3212. Original EPS figures have been converted to PNG files due to size limi

IMRT Beam Angle Optimization Using Non-descent Pattern Search Methods

https://thekeep.eiu.edu/commencement_spring2015/1304/thumbnail.jp

Optimization Problems in Radiation Therapy Treatment Planning.

Radiation therapy is one of the most common methods used to treat many types of cancer. External beam radiation therapy and the models associated with developing a treatment plan for a patient are studied. External beams of radiation are used to deliver a highly complex so-called dose distribution to a patient that is designed to kill the cancer cells while sparing healthy organs and normal tissue. Treatment planning models and optimization are used to determine the delivery machine instructions necessary to produce a desirable dose distribution. These instructions make up a treatment plan. This thesis studies four problems in radiation therapy treatment plan optimization. First, treatment planners generate a plan with a number of competing treatment plan criteria. The relationship between criteria is not known a priori. A methodology is developed for physicians and treatment planners to efficiently navigate a clinically relevant region of the Pareto frontier generated by trading off these different criteria in an informed way. Second, the machine instructions for intensity modulated radiation therapy, a common treatment modality, consist of the locations of the external beams and the non-uniform intensity profiles delivered from each of these locations. These decisions are traditionally made with separate, sequential models. These decisions are integrated into a single model and propose a heuristic solution methodology. Third, volumetric modulated arc therapy (VMAT), a treatment modality where the beam travels in a coplanar arc around the patient while continuously delivering radiation, is a popular topic among optimizers studying treatment planning due to the difficult nature of the problem and the lack of a universally accepted treatment planning method. While current solution methodologies assume a predetermined coplanar path around the patient, that assumption is relaxed and the generation of a non-coplanar path is integrated into a VMAT planning algorithm. Fourth, not all patient information is available when developing a treatment plan pre-treatment. Some information, like a patient's sensitivity to radiation, can be realized during treatment through physiological tests. Methodologies of pre-treatment planning considering adaptation to new information are studied.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113366/1/troylong_1.pd

Intensity Modulated Proton Therapy Optimization Under Uncertainty: Field Misalignment and Internal Organ Motion

Intensity modulated proton therapy (IMPT) is one of the most advanced forms of radiation therapy, which can deliver a highly conformal dose to the tumor while sparing the dose in healthy tissues. Compared to conventional photon-based radiation therapy, IMPT is more flexible in delivering radiation dose according to different tumor shapes. However, this flexibility also makes the optimization problems in IMPT harder to solve, e.g., it requires larger memory to store data and longer computational time. Furthermore, proton beams are very sensitive to different uncertainties, such as setup uncertainty, range uncertainty and internal organ motion. These uncertainties can greatly impact the quality of clinical treatment. Therefore, this dissertation aims to investigate different optimization methods for treatment planning and to handle a variety of uncertainties in IMPT. First, to solve the fluence map optimization (FMO) problem in IMPT, we propose a method to formulate the FMO problem into a molecular dynamics model. So that, the FMO problem can be optimized according classical dynamics system. This method combines the advantages of gradient-based algorithms and heuristic search algorithms. Next, we develop and validate a robust optimization method for IMPT treatment plans with multi-isocenter large fields to overcome the dose inhomogeneity problem caused by the setup misalignment in field junctions. Numerical results show that the robust optimized IMPT plans create a low gradient field radiation dose in the junction regions, which can minimize the impact from misalignment uncertainty. Compare to conventional techniques, the robust optimization method leads the whole treatment much more efficient. Lastly, we focus on a two-stage method to solve the beam angle optimization (BAO) problem in IMPT with internal organ motion uncertainty. In the first stage, a $p$-median algorithm is developed for beam angle clustering. In the second stage, a bi-level search algorithm is used to find the final beam angle set for the treatment. Furthermore, Support vector machine (SVM) is used for beam angle classification to reduce the search space and the 4D-CT information is incorporated to handle the internal organ motion uncertainty. Results show that the two-stage BAO method consistently finds a high-quality solution in a short time.Industrial Engineering, Department o