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Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting

Stammer, P.; Burigo, L.; J kel, O.; Frank, M.; Wahl, N.

Objective. To present an efficient uncertainty quantification method for range and set-up errors in Monte Carlo (MC) dose calculations. Further, we show that uncertainty induced by interplay and other dynamic influences may be approximated using suitable error correlation models. Approach. We introduce an importance (re-)weighting method in MC history scoring to concurrently construct estimates for error scenarios, the expected dose and its variance from a single set of MC simulated particle histories. The approach relies on a multivariate Gaussian input and uncertainty model, which assigns probabilities to the initial phase space sample, enabling the use of different correlation models. Through modification of the phase space parameterization, accuracy can be traded between that of the uncertainty or the nominal dose estimate. Main results. The method was implemented using the MC code TOPAS and validated for proton intensity-modulated particle therapy (IMPT) with reference scenario estimates. We achieve accurate results for set-up uncertainties (γ2 mm/2% ≥ 99.01% (E[d]), γ2 mm/2% ≥ 98.04% (σ(d))) and expectedly lower but still sufficient agreement for range uncertainties, which are approximated with uncertainty over the energy distribution. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000139340
Veröffentlicht am 29.10.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Steinbuch Centre for Computing (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0031-9155, 1361-6560
KITopen-ID: 1000139340
Erschienen in Physics in Medicine and Biology
Verlag IOP Publishing
Band 66
Heft 20
Seiten 205003
Nachgewiesen in Web of Science
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