Abstract
RapidArc is a novel treatment planning and delivery system that has recently been made available for clinical use. Included within the Eclipse treatment planning system are a number of different optimization strategies that can be employed to improve the quality of the final treatment plan. The purpose of this study is to systematically assess three categories of strategies for four phantoms, and then apply proven strategies to clinical head and neck cases. Four phantoms were created within Eclipse with varying shapes and locations for the planning target volumes and organs at risk. A baseline optimization consisting of a single 359.8° arc with collimator at 45° was applied to all phantoms. Three categories of strategies were assessed and compared to the baseline strategy. They include changing the initialization parameters, increasing the total number of control points, and increasing the total optimization time. Optimization log files were extracted from the treatment planning system along with final dose‐volume histograms for plan assessment. Treatment plans were also generated for four head and neck patients to determine whether the results for phantom plans can be extended to clinical plans. The strategies that resulted in a significant difference from baseline were: changing the maximum leaf speed prior to optimization (p<0.05), increasing the total number of segments by adding an arc (p<0.05), and increasing the total optimization time by either continuing the optimization (p<0.01) or adding time to the optimization by pausing the optimization (p<0.01). The reductions in objective function values correlated with improvements in the dose‐volume histogram (DVH). The addition of arcs and pausing strategies were applied to head and neck cancer cases, which demonstrated similar benefits with respect to the final objective function value and DVH. Analysis of the optimization log files is a useful way to intercompare treatment plans that have the same dose‐volume objectives and importance values. The results for clinical head and neck plans were consistent with phantom plans.PACS number: 87.55.x, 87.55.D, 87.55.de 87.55.dk
Highlights
Oliver et al.: RapidArc optimization log file formulated the optimization problem over two steps
This is a technique whereby the optimization begins with very coarse sampling of arc control points and as the optimization progresses, additional arc control points are added such that the final treatment plan has control points which are sampled at gantry positions approximately every 2°.(4) The two main advantages to progressive sampling are that it reduces the optimization time and that it circumvents the highly restrictive leaf motion constraints early in the optimization by exploring an arc that is coarsely sampled and, allows for large leaf movements between successive coarse samples
For the strategies that increase the total optimization time, continuing the optimization from level 2 (ROFVContMR2 = 0.609 ± 0.133, p < 0.05) and pausing the optimization for 7.5 minutes and 15 minutes resulted in significantly reduced objective function as compared to baseline
Summary
Oliver et al.: RapidArc optimization log file formulated the optimization problem over two steps. Volumetric-modulated arc therapy (VMAT) is an arc therapy technique that is optimized in one step with a progressive sampling algorithm This is a technique whereby the optimization begins with very coarse sampling of arc control points and as the optimization progresses, additional arc control points are added such that the final treatment plan has control points which are sampled at gantry positions approximately every 2°.(4) The two main advantages to progressive sampling are that it reduces the optimization time and that it circumvents the highly restrictive leaf motion constraints early in the optimization by exploring an arc that is coarsely sampled and, allows for large leaf movements between successive coarse samples. One difference is that VMAT adds arc control points one at a time during optimization, whereas RapidArc adds additional arc control points in groups which are called multi-resolution (MR) levels. In VMAT, only one stochastic change of either MLC position or dose rate per iteration is allowed whereas, with RapidArc, there are seven random changes to either dose rate or MLC positions per iteration.[11]
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