A Monte Carlo model of noise components in 3D PET

I. Castiglioni, O. Cremonesi, M. C. Gilardi, A. Savi, V. Bettinardi, G. Rizzo, E. Bellotti, F. Fazio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, a model to estimate noise components (scatter and random coincidences) in 3D PET was developed by Monte Carlo methods. The model allows the amount and the spatial distribution of true, scattered and random coincidences to be independently estimated for any radioactive source (both phantoms and real patients), properly accounting for system dead time. The model was applied to a 3D NaI(Tl) current generation PET scanner for which no methods to estimate scatter and random components in whole-body studies are currently available. The quantitative accuracy of the developed noise model was proved by comparison between simulated and measured PET scanner physical performance. Scatter and random components were assessed for brain and thorax studies giving evidence to the high scatter contribution and to the relatively low random component in whole-body 3D PET studies. The clinical response of the PET system, in terms of signal-to-noise ratio, was assessed and optimized, confirming the suitability of the default energy window, although suggesting a possible improvement by setting a lower energy threshold higher than the current default one. The proposed noise model applies to any current generation 3D PET scanner and was included in the Monte Carlo software package PET-EGS, devoted to 3D PET and freely available from authors.

Original languageEnglish
Title of host publicationIEEE Nuclear Science Symposium and Medical Imaging Conference
Pages2036-2039
Number of pages4
Volume4
Publication statusPublished - 2002
Event2001 IEEE Nuclear Science Symposium Conference Record - San Diego, CA, United States
Duration: Nov 4 2001Nov 10 2001

Other

Other2001 IEEE Nuclear Science Symposium Conference Record
CountryUnited States
CitySan Diego, CA
Period11/4/0111/10/01

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Industrial and Manufacturing Engineering

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