Mixed integer programming improves comprehensibility and plan quality in inverse optimization of prostate HDR-brachytherapy

Abstract: Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the solver at an early stage, without deterioration of the plan quality. For a fixed catheter configuration, the dwell time optimization model LP solves to optimality in less than 15 seconds, which confirms earlier results. We propose an iterative procedure for QP that allows to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iterative procedure is comparable in speed to the LP model, and produces better plans than the non-iterative QP. We formulate a new dose-volume based model that maximizes $V_{100\%}$ while satisfying pre-set DVH-criteria. This model optimizes both catheter positions and dwell times within a few minutes depending on prostate volume and number of catheters, optimizes dwell times within 35 seconds, and gives better DVH statistics than dose-based models. The solutions suggest that the correlation between objective value and clinical plan quality is weak in existing dose-based models.