Knowledge of the uncertainty associated with Monte Carlo estimates is useful for determining when to stop a simulation run when statistical uctuations fall below a desired tolerance level, and for designing and analyzing variance reduction techniques. In this work, we discuss how to analytically calculate the uncertainty of Monte Carlo variance estimates from higher order moments of the distribution of events. In addition, we show how these expressions can be incorporated in the study of x-ray imaging detectors to manage runtime and precision of the simulation. Our analysis can be used to design variance reduction techniques for Monte Carlo simulations when, as in many cases in imaging, the variance and not the mean, is the quantity of interest.

Date Published: 19 March 2013
PDF: 4 pages
Proc. SPIE 8668, Medical Imaging 2013: Physics of Medical Imaging, 86680L (19 March 2013); doi: 10.1117/12.2007432

Published in SPIE Proceedings Vol. 8668:
Medical Imaging 2013: Physics of Medical Imaging
Robert M. Nishikawa; Bruce R. Whiting; Christoph Hoeschen, Editor(s)