Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting
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 introd...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
7 October 2021
|
| In: |
Physics in medicine and biology
Year: 2021, Jahrgang: 66, Heft: 20, Pages: 1-20 |
| ISSN: | 1361-6560 |
| DOI: | 10.1088/1361-6560/ac287f |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1088/1361-6560/ac287f
|
| Verfasserangaben: | P. Stammer, L. Burigo, O. Jäkel, M. Frank and N. Wahl |
MARC
| LEADER | 00000caa a2200000 c 4500 | ||
|---|---|---|---|
| 001 | 1778228062 | ||
| 003 | DE-627 | ||
| 005 | 20230426075248.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 211122s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1088/1361-6560/ac287f |2 doi | |
| 035 | |a (DE-627)1778228062 | ||
| 035 | |a (DE-599)KXP1778228062 | ||
| 035 | |a (OCoLC)1341424093 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a 33 |2 sdnb | ||
| 100 | 1 | |a Stammer, Pia |e VerfasserIn |0 (DE-588)1246217287 |0 (DE-627)1778229123 |4 aut | |
| 245 | 1 | 0 | |a Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting |c P. Stammer, L. Burigo, O. Jäkel, M. Frank and N. Wahl |
| 264 | 1 | |c 7 October 2021 | |
| 300 | |a 20 | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Gesehen am 22.11.2021 | ||
| 520 | |a 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 (γ 2 mm/2% ≥ 99.01% (E[ d ]), γ 2 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. Here pass rates of 99.39% (E[ d ])/ 93.70% (σ( d )) (range errors) and 99.86% (E[ d ])/ 96.64% (σ( d )) (range and set-up errors) can be achieved. Initial evaluations on a water phantom, a prostate and a liver case from the public CORT dataset show that the CPU time decreases by more than an order of magnitude. Significance. The high precision and conformity of IMPT comes at the cost of susceptibility to treatment uncertainties in particle range and patient set-up. Yet, dose uncertainty quantification and mitigation, which is usually based on sampled error scenarios, becomes challenging when computing the dose with computationally expensive but accurate MC simulations. As the results indicate, the proposed method could reduce computational effort while also facilitating the use of high-dimensional uncertainty models. | ||
| 700 | 1 | |a Burigo, Lucas Norberto |d 1985- |e VerfasserIn |0 (DE-588)1053988648 |0 (DE-627)790906627 |0 (DE-576)409946028 |4 aut | |
| 700 | 1 | |a Jäkel, Oliver |d 1964- |e VerfasserIn |0 (DE-588)1050056302 |0 (DE-627)783260709 |0 (DE-576)404358705 |4 aut | |
| 700 | 1 | |a Frank, M. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Wahl, Niklas |d 1988- |e VerfasserIn |0 (DE-588)107775177X |0 (DE-627)837441838 |0 (DE-576)445344539 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Physics in medicine and biology |d Bristol : IOP Publ., 1956 |g 66(2021), 20, Artikel-ID 205003, Seite 1-20 |h Online-Ressource |w (DE-627)269016163 |w (DE-600)1473501-5 |w (DE-576)088704130 |x 1361-6560 |7 nnas |a Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting |
| 773 | 1 | 8 | |g volume:66 |g year:2021 |g number:20 |g elocationid:205003 |g pages:1-20 |g extent:20 |a Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting |
| 856 | 4 | 0 | |u https://doi.org/10.1088/1361-6560/ac287f |x Verlag |x Resolving-System |z lizenzpflichtig |3 Volltext |
| 951 | |a AR | ||
| 992 | |a 20211122 | ||
| 993 | |a Article | ||
| 994 | |a 2021 | ||
| 998 | |g 107775177X |a Wahl, Niklas |m 107775177X:Wahl, Niklas |d 50000 |e 50000PW107775177X |k 0/50000/ |p 5 |y j | ||
| 998 | |g 1050056302 |a Jäkel, Oliver |m 1050056302:Jäkel, Oliver |d 50000 |e 50000PJ1050056302 |k 0/50000/ |p 3 | ||
| 998 | |g 1053988648 |a Burigo, Lucas Norberto |m 1053988648:Burigo, Lucas Norberto |d 50000 |e 50000PB1053988648 |k 0/50000/ |p 2 | ||
| 999 | |a KXP-PPN1778228062 |e 400464125X | ||
| BIB | |a Y | ||
| SER | |a journal | ||
| JSO | |a {"id":{"doi":["10.1088/1361-6560/ac287f"],"eki":["1778228062"]},"name":{"displayForm":["P. Stammer, L. Burigo, O. Jäkel, M. Frank and N. Wahl"]},"type":{"bibl":"article-journal","media":"Online-Ressource"},"language":["eng"],"note":["Gesehen am 22.11.2021"],"title":[{"title":"Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting","title_sort":"Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting"}],"origin":[{"dateIssuedKey":"2021","dateIssuedDisp":"7 October 2021"}],"person":[{"role":"aut","given":"Pia","family":"Stammer","display":"Stammer, Pia"},{"display":"Burigo, Lucas Norberto","family":"Burigo","given":"Lucas Norberto","role":"aut"},{"display":"Jäkel, Oliver","family":"Jäkel","given":"Oliver","role":"aut"},{"family":"Frank","role":"aut","given":"M.","display":"Frank, M."},{"given":"Niklas","role":"aut","family":"Wahl","display":"Wahl, Niklas"}],"relHost":[{"disp":"Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weightingPhysics in medicine and biology","physDesc":[{"extent":"Online-Ressource"}],"recId":"269016163","note":["Gesehen am 28.01.2019","Fortsetzung der Druck-Ausgabe"],"type":{"media":"Online-Ressource","bibl":"periodical"},"language":["eng"],"part":{"year":"2021","volume":"66","pages":"1-20","issue":"20","text":"66(2021), 20, Artikel-ID 205003, Seite 1-20","extent":"20"},"title":[{"subtitle":"an official journal of the Institute of Physics and Engineering in Medicine","title_sort":"Physics in medicine and biology","title":"Physics in medicine and biology"}],"id":{"zdb":["1473501-5"],"eki":["269016163"],"issn":["1361-6560"]},"pubHistory":["1.1956 -"],"origin":[{"publisher":"IOP Publ.","publisherPlace":"Bristol","dateIssuedKey":"1956","dateIssuedDisp":"1956-"}]}],"physDesc":[{"extent":"20 S."}],"recId":"1778228062"} | ||
| SRT | |a STAMMERPIAEFFICIENTU7202 | ||