Poster — Wed Eve—47: IMRT Beam Angle Optimization Using a Hot‐Scripted Learning Algorithm for a Commercial Planning System

Traditionally, beam angle selection for intensity‐modulated radiation therapy (IMRT) plans has been left up to the experienced dosimetrist. However, the large number of potential angle orientations suggests that the human plan may not be ideal. A learning algorithm (a variation of a genetic algorithm) was written to select beam angles to produce plans with more desirable dose‐volume histograms (DVH). The algorithm is used in parallel with the commercial Pinnacle 3 Radiation Therapy Planning System. Starting with a generation of randomly chosen beam angles, the algorithm uses Pinnacle's built‐in hot scripting to call on the P 3 IMRT portion of the software to perform dose calculations on each individual. Each set of angles is then ranked using a dosimetric fitness function that uses the same constraints that are used during the IMRT calculations. A new generation of beam angles is then constructed using both biologically and non‐biologically relevant operators. Operator weights are also adjusted each generation based on the dosimetric fitness function as well. Once completed, the algorithm was tested on several IMRT prostate cases. In each case, the DVH for the algorithm‐selected plan fit the dose constraints better than the human‐designed IMRT plan. A major drawback was the long running time of the algorithm (up to 11 hours), which was constrained almost entirely by the speed of Pinnacle's IMRT calculations. The ideal use of the code would be for overnight applications, with the human planner then using the optimized beam angles to plan as normal. This research was supported by the Graduate Internship program of the MITACS Network of Centres of Excellence and the Local Investigator Research Fund of the Windsor Regional Cancer Centre.