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Program Information

Automated Two Loop Optimization of a Treatment Planning System

H Wang

H Wang1*, L Xing2 , (1) Stanford Univ School of Medicine, Stanford, CA, (2) Stanford Univ School of Medicine, Stanford, CA


SU-E-J-75 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: To establish a strategy of a two loop optimization with recorded interactions between a planner and a commercial treatment planning system (TPS) and to apply it to facilitate VMAT/IMRT planning with incorporation of prior knowledge-guidance.

Methods: We first record some commonly used planner-TPS interactions as subroutines using Microsoft Visual Studio Coded UI. A recorded action is called back in C# application programming when the corresponding task needs to be accomplished. We implement a prior knowledge-guided VMAT/IMRT plan selection algorithm in a two loop optimization framework with an Eclipse TPS (Varian Medical Systems, Palo Alto, CA). In this implementation, the DVHs of a prior treatment plan are used to guide the search for a clinically optimal VMAT/IMRT treatment plan by iteratively evaluating and modifying the optimization parameters of a series of Eclipse plans. The approach is applied to the treatment planning of three clinical cases, a prostate case and two head and neck cases.

Results: An automated two loop optimization is developed. The method is applied to guide VMAT/IMRT planning process and our results show that it is capable of finding clinically sensible treatment plans with little planner interactions. The process mimics a planner’s planning process and provides a solution that would otherwise require a huge amount of trial-and-error interactions of a planner. The results obtained using the approach are found to be either clinically acceptable or close to be acceptable.

Conclusion: The proposed technique provides a valuable way to harness a commercial TPS by application programming via the use of recorded human-computer interactions. A prior knowledge guided plan selection is developed in the platform, which greatly facilitates the search of clinically acceptable plans. The development brings us a big step closer toward the goal of automated treatment planning in a clinical environment.

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