liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Estimating statistical uncertainty of Monte Carlo efficiency-gain in the context of a correlated sampling Monte Carlo code for brachytherapy treatment planning with non-normal dose distribution
Virginia Commonwealth Univ, Richmond, USA.
Virginia Commonwealth Univ, Richmond, USA.
Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.ORCID iD: 0000-0003-0209-498X
Show others and affiliations
2012 (English)In: Applied Radiation and Isotopes, ISSN 0969-8043, E-ISSN 1872-9800, Vol. 70, no 1, 315-323 p.Article in journal (Refereed) Published
Abstract [en]

Correlated sampling Monte Carlo methods can shorten computing times in brachytherapy treatment planning. Monte Carlo efficiency is typically estimated via efficiency gain, defined as the reduction in computing time by correlated sampling relative to conventional Monte Carlo methods when equal statistical uncertainties have been achieved. The determination of the efficiency gain uncertainty arising from random effects, however, is not a straightforward task specially when the error distribution is non-normal. The purpose of this study is to evaluate the applicability of the F distribution and standardized uncertainty propagation methods (widely used in metrology to estimate uncertainty of physical measurements) for predicting confidence intervals about efficiency gain estimates derived from single Monte Carlo runs using fixed-collision correlated sampling in a simplified brachytherapy geometry. A bootstrap based algorithm was used to simulate the probability distribution of the efficiency gain estimates and the shortest 95% confidence interval was estimated from this distribution. It was found that the corresponding relative uncertainty was as large as 37% for this particular problem. The uncertainty propagation framework predicted confidence intervals reasonably well; however its main disadvantage was that uncertainties of input quantities had to be calculated in a separate run via a Monte Carlo method. The F distribution noticeably underestimated the confidence interval. These discrepancies were influenced by several photons with large statistical weights which made extremely large contributions to the scored absorbed dose difference. The mechanism of acquiring high statistical weights in the fixed-collision correlated sampling method was explained and a mitigation strategy was proposed.

Place, publisher, year, edition, pages
UK: Pergamon , 2012. Vol. 70, no 1, 315-323 p.
Keyword [en]
Monte Carlo, Correlated sampling, Efficiency, Uncertainty, Bootstrap
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-72277DOI: 10.1016/j.apradiso.2011.09.015ISI: 000297901400052OAI: diva2:458962
Available from: 2011-11-28 Created: 2011-11-24 Last updated: 2015-03-20Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Alm Carlsson, GudrunMalusek, Alexandr
By organisation
Radiation PhysicsFaculty of Health Sciences
In the same journal
Applied Radiation and Isotopes
Medical and Health Sciences

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 58 hits
ReferencesLink to record
Permanent link

Direct link