GPU-accelerated bi-objective treatment planning for prostate high-dose-rate brachytherapy

Purpose: The purpose of this study is to improve upon a recently introduced bi-objective treatment planning method for prostate high-dose-rate (HDR) brachytherapy (BT), both in terms of resulting

Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy

Purpose: Prostate high-dose-rate brachytherapy (HDR-BT) planning involves determining the movement that a high-strength radiation stepping source travels through the patient's body, such that the resulting radiation dose distribution sufficiently covers tumor volumes and safely spares nearby healthy organs from radiation risks. The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been shown to be able to effectively handle this inherent bi-objective nature of HDR-BT planning. However, in clinical practice there is a very restricted planning time budget (often less than 1 h) for HDR-BT planning, and a considerable amount of running time needs to be spent before MO-RV-GOMEA finds a good trade-off front of treatment plans (about20–30 min on a single CPU core) with sufficiently accurate dose calculations, limiting the applicability of the approach in the clinic. To address this limitation, we propose an efficiency enhancement technique for MO-RV-GOMEA solving the bi-objective prostate HDR-BT planning problem.Methods: Dose-Volume (DV) indices are often used to assess the quality of HDR-BT plans. The accuracy of these indices depends on the number of dose calculation points at which radiation doses are computed. These are randomly uniformly sampled inside target volumes and organs at risk. In available HDR-BT planning optimization algorithms, the number of dose calculation points is fixed. The more points are used, the better the accuracy of the obtained results will be, but also the longer the algorithms need to be run. In this work, we introduce a so-called multi-resolution scheme that gradually increases the number of dose calculation points during the optimization run such that the running time can be substantially reduced without compromising on the accuracy of the obtained results.Results and conclusion: Experiments on a data set of 18 patient cases show that with the multi-resolution scheme, MO-RV-GOMEA can achieve a sufficiently good trade-off front of treatment plans after five minutes of running time on a single CPU core (4–6 times faster than the old approach with a fixed number of dose calculation points). When the optimization with the multi-resolution scheme is run on a quad-core machine, five minutes are enough to obtain trade-off fronts that are nearly as good as those obtained by running optimization with the old approach in one hour (i.e., 12 times faster). This leaves ample time to perform the selection of the preferred treatment plan from the trade-off front for the specific patient at hand. Furthermore, comparisons with real clinical treatment plans, which were manually made by experienced BT planners within 30–60 min, confirm that the plans obtained by our approach are superior in terms of DV indices. These results indicate that our proposed approach has the potential to be employed in clinical practice.</p

Robust optimization for HDR prostate brachytherapy applied to organ reconstruction uncertainty

Purpose. Recently, we introduced a bi-objective optimization approach based on dose-volume indices to automatically create clinically goodHDRprostate brachytherapy plans. To calculate dose-volume indices, a reconstruction algorithm is used to determine the 3D organ shape from 2D contours, inevitably containing settings that influence the result.Weaugment the optimization approach to quickly find plans that are robust to differences in 3D reconstruction. Methods. Studied reconstruction settings were: Interpolation between delineated organ contours, overlap between contours, and organ shape at the top and bottom contour. Two options for each setting yields 8 possible 3D organ reconstructions per patient, over which the robust model defines minimax optimization. For the original model, settings were based on our treatment planning system. Both models were tested on data of 26 patients and compared by re-evaluating selected optimized plans both in the original model (1 organ reconstruction, the difference determines the cost), and in the robust model (8 organ reconstructions, the difference determines the benefit). Results. Robust optimization increased the run time from 3 to 6 min. The median cost for robust optimization as observed in the original model was-0.25% in the dose-volume indices with a range of [-0.01%,-1.03%]. The median benefit of robust optimization as observed in the robust model was 0.93% with a range of [0.19%, 4.

Bi-objective optimization of catheter positions for high-dose-rate prostate brachytherapy

Purpose: Bi-objective simultaneous optimization of catheter positions and dwell times for high-dose-rate (HDR) prostate brachytherapy, based directly on dose-volume indices, has shown promising results. However, optimization with the state-of-the-art evolutionary algorithm MO-RV-GOMEA so far required several hours of runtime, and resulting catheter positions were not always clinically feasible. The aim of this study is to extend the optimization model and apply GPU parallelization to achieve clinically acceptable computation times. The resulting optimization procedure is compared with a previously introduced method based solely on geometric criteria, the adapted Centroidal Voronoi Tessellations (CVT) algorithm. Methods: Bi-objective simultaneous optimiz