Multiobjective anatomy-based dose optimization for HDR-brachytherapy with constraint free deterministic algorithms

Abstract

In high dose rate (HDR) brachytherapy, conventional dose optimization algorithms consider multiple objectives in the form of an aggregate function that transforms the multiobjective problem into a single-objective problem. As a result, there is a loss of information on the available alternative possible solutions. This method assumes that the treatment planner exactly understands the correlation between competing objectives and knows the physical constraints. This knowledge is provided by the Pareto trade-off set obtained by single-objective optimization algorithms with a repeated optimization with different importance vectors. A mapping technique avoids non-feasible solutions with negative dwell weights and allows the use of constraint free gradient-based deterministic algorithms. We compare various such algorithms and methods which could improve their performance. This finally allows us to generate a large number of solutions in a few minutes. We use objectives expressed in terms of dose variances obtained from a few hundred sampling points in the planning target volume (PTV) and in organs at risk (OAR). We compare two- to four-dimensional Pareto fronts obtained with the deterministic algorithms and with a fast-simulated annealing algorithm. For PTV-based objectives, due to the convex objective functions, the obtained solutions are global optimal. If OARs are included, then the solutions found are also global optimal, although local minima may be present as suggested.

Use of Radiobiological Modeling in Treatment Plan Evaluation and Optimization of Prostate Cancer Radiotherapy

“…Compared to other 3D imaging modalities (CT, MR) U/S can provide real-time, accurate 3D information on the size and the position of the target volume, on the position of the organs-at-risk and the real time needle tracking and navigation. The use of inverse planning in HDR brachytherapy results in a fast planning process that produces reproducible high quality treatment plans that closely match the clinical protocol constraints (Baltas & Zamboglou 2006, Hsu et al 2008, Martinez et al 1989, Milickovic et al 2002. During the last decade a number of inverse planning algorithms have been proposed (Alterovitz et al 2006, Karabis et al 2009, Lahanas et al 1999 and many of them have been implemented in modern Treatment Planning Systems (TPS) (Oncentra Prostate™, Nucletron B.V., Veenendaal, The Netherlands, Oncentra Brachy™, Nucletron B.V., Veenendaal, The Netherlands, BrachyVision Treatent Planning™, Varian Medical Systems).…”

Immunizing Conic Quadratic Optimization Problems Against Implementation Errors

“…These dwell times cannot exactly be realized in practice, since the dwell times have to be multiples of say 0.1 seconds. For more details see [10]. There are also many examples of optimization problems that are not conic quadratic, but can be reformulated as such.…”

Interactive multiobjective optimization for anatomy-based three-dimensional HDR brachytherapy

“…The multiobjective optimization approaches presented in the literature are based on using objective weights defined beforehand, where the final objective function is expressed as a weighted sum of the conflicting objectives (e.g. Milickovic et al 2002, Lahanas andBaltas 2003). In these cases, objectives are often formulated as using penalties where doses exceeding predefined upper limits are penalized (e.g.…”

“…As mentioned, the studies where brachytherapy treatment plans have been optimized are based on a priori methods (weighting method, e.g. Milickovic et al 2002) or a posteriori methods (evolutionary algorithms, e.g. Lahanas et al 2003b).…”