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One of the grand challenges in intensity-modulated radiotherapy (IMRT)planning is to optimize the beam angles in a reasonable computation time. Todemonstrate the value of prior knowledge on improving the efficiency of beamangle optimization (BAO) in IMRT, this study proposes a modified geneticalgorithm (GA) by incorporating prior knowledge into the evolutionaryprocedure. In the so-called nowGABAO (kNOWledge guided GA for BAO) approach,two types of prior knowledge are incorporated into the general flowchart of GA:(1) beam angle constraints, which define the angle scopes through which no beamis allowed to pass, and (2) plan templates, which define the most possible beamconfigurations suitable to the studied case and are used to guide theevolutionary progress of GA. Optimization tasks with different prior knowledgewere tested, and the results on two complicated clinical cases at thehead-and-neck and lung regions show that suitable defining of angle constraintsand plan templates may obviously speedup the optimization. Furthermore, amoderate number of quite bad templates produce negligible influence on thedegradation of optimization efficiency. In conclusion, prior knowledge helpsimprove the beam angle optimization efficiency, and can be suitablyincorporated by a GA. It is resistant to the inclusion of bad templates.

Prior Knowledge Helps Improve Beam Angle Optimization Efficiency in Radiotherapy Planning