TBS-BAO: fully automated beam angle optimization for IMRT guided by a total-beam-space reference plan

Abstract

Abstract Properly selected beam angles contribute to the quality of radiotherapy treatment plans. However, the beam angle optimization (BAO) problem is difficult to solve to optimality due to its non-convex discrete nature with many local minima. In this study, we propose TBS-BAO, a novel approach for solving the BAO problem, and test it for non-coplanar robotic CyberKnife radiotherapy for prostate cancer. First, an ideal Pareto-optimal reference dose distribution is automatically generated using a priori multi-criterial fluence map optimization (FMO) to generate a plan that includes all candidate beams (total-beam-space, TBS). Then, this ideal dose distribution is reproduced as closely as possible in a subsequent segmentation/beam angle optimization step (SEG/BAO), while limiting the number of allowed beams to a user-selectable preset value. SEG/BAO aims at a close reproduction of the ideal dose distribution. For each of 33 prostate SBRT patients, 18 treatment plans with different pre-set numbers of allowed beams were automatically generated with the proposed TBS-BAO. For each patient, the TBS-BAO plans were then compared to a plan that was automatically generated with an alternative BAO method (Erasmus-iCycle) and to a high-quality manually generated plan. TBS-BAO was able to automatically generate plans with clinically feasible numbers of beams (∼25), with a quality highly similar to corresponding 91-beam ideal reference plans. Compared to the alternative Erasmus-iCycle BAO approach, similar plan quality was obtained for 25-beam segmented plans, while computation times were reduced from 10.7 hours to 4.8/1.5 hours, depending on the applied pencil-beam resolution in TBS-BAO. 25-beam TBS-BAO plans had similar quality as manually generated plans with on average 48 beams, while delivery times reduced from 22.3 to 18.4/18.1 min. TBS reference plans could effectively steer the discrete non-convex BAO.