nonparametric residue analysis of dynamic PET data with application to cerebral FDG studies in normals

Finbarr O'sullivan, Mark Muzi, Alexander M. Spence, David M. Mankoff, Janet N. O'sullivan, Niall Fitzgerald, George C. Newman, Kenneth A. Krohn

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Kinetic analysis is used to extract metabolic information from dynamic positron emission tomography (PET) uptake data. The theory of indicator dilutions, developed in the seminal work of Meier and Zierler (1954), provides a probabilistic framework for representation of PET tracer uptake data in terms of a convolution between an arterial input function and a tissue residue. The residue is a scaled survival function associated with tracer residence in the tissue. Nonparametric inference for the residue, a deconvolution problem, provides a novel approach to kinetic analysis-critically one that is not reliant on specific compartmental modeling assumptions. A practical computational technique based on regularized cubic B-spline approximation of the residence time distribution is proposed. Nonparametric residue analysis allows formal statistical evaluation of specific parametric models to be considered. This analysis needs to properly account for the increased flexibility of the nonparametric estimator. The methodology is illustrated using data from a series of cerebral studies with PET and fluorodeoxyglucose (FDG) in normal subjects. Comparisons are made between key functionals of the residue, tracer flux, flow, etc., resulting from a parametric (the standard two-compartment of Phelps et al. 1979) and a nonparametric analysis. Strong statistical evidence against the compartment model is found. Primarily these differences relate to the representation of the early temporal structure of the tracer residencelargely a function of the vascular supply network. There are convincing physiological arguments against the representations implied by the compartmental approach but this is the first time that a rigorous statistical confirmation using PET data has been reported. The compartmental analysis produces suspect values for flow but, notably, the impact on the metabolic flux, though statistically significant, is limited to deviations on the order of 3%-4%. The general advantage of the nonparametric residue analysis is the ability to provide a valid kinetic quantitation in the context of studies where there may be heterogeneity or other uncertainty about the accuracy of a compartmental model approximation of the tissue residue.

Original languageEnglish (US)
Pages (from-to)556-571
Number of pages16
JournalJournal of the American Statistical Association
Volume104
Issue number486
DOIs
StatePublished - Jun 1 2009

Keywords

  • Deconvolution
  • Functional inference
  • Kinetic analysis
  • Regularization

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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    O'sullivan, F., Muzi, M., Spence, A. M., Mankoff, D. M., O'sullivan, J. N., Fitzgerald, N., Newman, G. C., & Krohn, K. A. (2009). nonparametric residue analysis of dynamic PET data with application to cerebral FDG studies in normals. Journal of the American Statistical Association, 104(486), 556-571. https://doi.org/10.1198/jasa.2009.0021