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Monte Carlo simulation of radiological imaging systems and the recovery of the Poisson distribution

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2 Author(s)
J. Peter ; Duke Univ. Med. Center, Durham, NC, USA ; R. J. Jaszczak

The Monte Carlo (MC) method is impracticable for simulating the physical processes of particle transport in three-dimensional imaging systems without the use of variance reduction (VR) techniques. As a consequence of VR, not photon counts but weights are accumulated which do not generate a Poisson mixture. Here, the authors analyze MC simulated data regarding (a) the type of distribution generated, (b) the problem of Poisson mixture recovery, (c) quantiative MC and (d) a stopping criteria for MC simulations. In order to perform this investigation, a MC simulation program which includes photon-specific forced detection/interaction VR techniques is used. By computing a generalized linear model estimates and moments of simulated distributions, the authors found that there exists a scaling factor which scales any uni-variate un-attenuated distribution into a corresponding Poisson distribution. If attenuation is present, the authors extend the simulated exponential mixture by an un-attenuated population and use the moments of this reference sample to calculate a scaling factor which recovers a complete finite Poisson mixture. The presented results could increase the potential applicability of MC simulations in nuclear medicine by performing quantiative simulations and by reducing computational load by a count-based stopping criteria. As a further result of this investigation, the authors confirmed that the error introduced by the included VR techniques is marginal for the simulated systems

Published in:

Nuclear Science Symposium Conference Record, 2000 IEEE  (Volume:3 )

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