An illustration of the use of model-based bootstrapping for evaluation of uncertainty in kinetic information derived from dynamic PET
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Date
2019-11
Authors
Gu, Fengyun
Wu, Qi
O'Sullivan, Finbarr
Huang, Jian
Muzi, Mark
Mankoff, David A.
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Institute of Electrical and Electronics Engineers (IEEE)
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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.
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Keywords
Cancer , Medical image processing , Mixture models , Positron emission tomography , Statistical analysis , 4-D PET data , Kinetic mapping , PET flow-metabolism imaging study , Dynamic PET imaging , Bootstrapped data , Model-based bootstrapping , Kinetic information , Mixture analysis , Comprehensive voxel-level analysis , Fitted mixture model , Statistical copies , Breast cancer patient , Image domain , Projection domain , Data models , Kinetic theory , Imaging , Analytical models , Numerical models , Standards , Uncertainty , Spatial autocorrelation , Model-based bootstrap , Standard errors , Simulation
Citation
Gu, F., Wu, Q., O’Sullivan, F., Huang, J., Muzi, M. and Mankoff, D. A. (2019) 'An Illustration of the Use of Model-Based Bootstrapping for Evaluation of Uncertainty in Kinetic Information Derived from Dynamic PET', 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Manchester, United Kingdom, 26 Oct.-2 Nov. doi: 10.1109/NSS/MIC42101.2019.9059736
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