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Robust Multi-Criteria IMPT Optimization


W Chen

W Chen*, J Unkelbach, A Trofimov, T Madden, H Kooy, T Bortfeld, D Craft, Massachusetts General Hospital, Boston, MA

TH-A-213AB-8 Thursday 8:00:00 AM - 9:55:00 AM Room: 213AB

Purpose:
We present a method to include robustness in a multi-criteria optimization (MCO) framework for intensity-modulated proton therapy (IMPT). The approach allows one to simultaneously explore the trade-off between different objectives including robustness and nominal plan quality.

Methods:
A database of plans each emphasizing different treatment planning objectives, is pre-computed to approximate the Pareto surface. An IMPT treatment plan that strikes the best balance between the different objectives can be selected by navigating on the Pareto surface. We integrate robustness into MCO by adding robustified objectives and constraints. Uncertainties are modeled by pre-calculated dose-influence matrices for a nominal scenario and a number of pre-defined error scenarios (shifted patient positions, proton beam undershoot and overshoot). A robustified objective represents the worst objective function value that can be realized for any of the error scenarios and thus provides a measure of plan robustness. The optimization method uses a linear projection solver and is capable of handling large problem sizes resulting from a fine dose grid resolution, many scenarios, and a large number of proton pencil beams.

Results:
A base-of-skull case is used to demonstrate that the robust optimization method reduces the sensitivity of the treatment plan to setup and range errors to a degree that is not achieved by a safety margin approach. A chordoma case is analyzed in more detail to demonstrate the involved trade-offs between target underdose and brainstem sparing as well as robustness and nominal plan quality. The robust optimization for each Pareto optimal plan takes less than 5 min on a standard computer.

Conclusions:
The uncertainty pertinent to the IMPT procedure can be reduced during treatment planning by optimizing a database of plans that emphasize different treatment objectives, including robustness. The planner can then interactively explore all convex combinations of database plans to decide on the most-preferred trade-off.

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