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000125214 0247_ $$2doi$$a10.1088/1361-6560/aa6ec5
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000125214 037__ $$aDKFZ-2017-01369
000125214 041__ $$aeng
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000125214 1001_ $$0P:(DE-He78)dfd5aaf608015baaaed0a15b473f1336$$aWahl, Niklas$$b0$$eFirst author$$udkfz
000125214 245__ $$aEfficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy.
000125214 260__ $$aBristol$$bIOP Publ.$$c2017
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000125214 520__ $$aThe sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU [Formula: see text] min). The resulting standard deviation (expectation value) of dose show average global [Formula: see text] pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity, while run-times of sampling-based computations are linear in the number of fractions. Using sum sampling within APM, uncertainty propagation can only be accelerated at the cost of reduced accuracy in variance calculations. For probabilistic plan optimization, we were able to approximate the necessary pre-computations within seconds, yielding treatment plans of similar quality as gained from exact uncertainty propagation. APM is suited to enhance the trade-off between speed and accuracy in uncertainty propagation and probabilistic treatment plan optimization, especially in the context of fractionation. This brings fully-fledged APM computations within reach of clinical application.
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000125214 7001_ $$aHennig, P.$$b1
000125214 7001_ $$0P:(DE-He78)59c02b7b30ad8972cf422bb1c955956c$$aWieser, H. P.$$b2$$udkfz
000125214 7001_ $$0P:(DE-He78)fec480a99b1869ec73688e95c2f0a43b$$aBangert, Mark$$b3$$eLast author$$udkfz
000125214 773__ $$0PERI:(DE-600)1473501-5$$a10.1088/1361-6560/aa6ec5$$gVol. 62, no. 14, p. 5790 - 5807$$n14$$p5790 - 5807$$tPhysics in medicine and biology$$v62$$x1361-6560$$y2017
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000125214 9141_ $$y2017
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