Prioritized efficiency optimization for intensity modulated proton therapy.

A high dosimetric quality and short treatment time are major goals in radiotherapy planning. Intensity modulated proton therapy (IMPT) plans obtain dose distributions of great conformity but often result in long delivery times which are typically not incorporated into the optimization process. We present an algorithm to optimize delivery efficiency of IMPT plans while maintaining plan quality, and study the potential trade-offs of these interdependent objectives. The algorithm is based on prioritized optimization, a stepwise approach to implemented objectives. First the quality of the plan is optimized. The second step of the prioritized efficiency optimization (PrEfOpt) routine offers four alternatives for reducing delivery time: minimization of the total spot weight sum (A), maximization of the lowest spot intensity of each energy layer (B), elimination of low-weighted spots (C) or energy layers (D). The trade-off between dosimetric quality (step I) and treatment time (step II) is controlled during the optimization by option-dependent parameters. PrEfOpt was applied to a clinical patient case, and plans for different trade-offs were calculated. Delivery times were simulated for two virtual facilities with constant and variable proton current, i.e. independent and dependent on the optimized spot weight distributions. Delivery times decreased without major degradation of plan quality; absolute time reductions varied with the applied method and facility type. Minimizing the total spot weight sum (A) reduced times by 28% for a similar plan quality at a constant current (changes of minimum dose in the target  <1%). For a variable proton current, eliminating low-weighted spots (C) led to remarkably faster delivery (16%). The implementation of an efficiency-optimization step into the optimization process can yield reduced delivery times with similar plan qualities. A potential clinical application of PrEfOpt is the generation of multiple plans with different trade-offs for a multicriteria optimization setting. Then, the planner can select the preferred compromise between treatment time and quality for each individual patient.