An Illustration of the Use of Model-Based Bootstrapping for Evaluation of Uncertainty in Kinetic Information Derived from Dynamic PET

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

Kinetic mapping via mixture analysis[8], [10] involves comprehensive voxel-level analysis of dynamic PET data. Bootstrapping from the fitted mixture model gives the ability to directly simulate statistical copies of the 4-D PET data, and following suitable analysis, subsequent simulations of the associated kinetic maps. This gives the ability to numerically evaluate uncertainties in inferences associated with kinetic information. We provide a simple introduction to the concept of the model-based bootstrap and an illustration of the use of the approach for kinetic mapping from dynamic PET using results from recent work in Huang et al.[4]. The illustration is from a PET flow-metabolism imaging study in a breast cancer patient. It involves separate dynamic PET imaging following injections of O-15 H2O and F-18 FDG. The bootstrapped data is created in the image domain rather than the projection domain, so there is no reconstruction requirement involved.

Original languageEnglish
Title of host publication2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141640
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019 - Manchester, United Kingdom
Duration: 26 Oct 20192 Nov 2019

Publication series

Name2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019

Conference

Conference2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
Country/TerritoryUnited Kingdom
CityManchester
Period26/10/192/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Mixture models
  • Model-based bootstrap
  • Simulation
  • Spatial autocorrelation
  • Standard errors

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