TY  - JOUR
AU  - Stammer, Pia
AU  - Burigo, L.
AU  - Jäkel, Oliver
AU  - Frank, M.
AU  - Wahl, Niklas
TI  - Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting.
JO  - Physics in medicine and biology
VL  - 66
IS  - 20
SN  - 1361-6560
CY  - Bristol
PB  - IOP Publ.
M1  - DKFZ-2021-02230
SP  - 205003 
PY  - 2021
N1  - #EA:E040#LA:E040#
AB  - 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
KW  - Monte Carlo (Other)
KW  - importance sampling (Other)
KW  - intensity modulated particle therapy (IMPT) (Other)
KW  - proton therapy (Other)
KW  - range error (Other)
KW  - setup error (Other)
KW  - uncertainty (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:34544068
DO  - DOI:10.1088/1361-6560/ac287f
UR  - https://inrepo02.dkfz.de/record/176997
ER  -