Literature DB >> 19830267

Nonparametric Residue Analysis of Dynamic PET Data With Application to Cerebral FDG Studies in Normals.

Finbarr O'Sullivan1, Mark Muzi, Alexander M Spence, David M Mankoff, Janet N O'Sullivan, Niall Fitzgerald, George C Newman, Kenneth A Krohn.   

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 residence-largely 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.

Entities:  

Year:  2009        PMID: 19830267      PMCID: PMC2760850          DOI: 10.1198/jasa.2009.0021

Source DB:  PubMed          Journal:  J Am Stat Assoc        ISSN: 0162-1459            Impact factor:   5.033


  29 in total

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Journal:  Nucl Med Commun       Date:  2004-07       Impact factor: 1.690

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Journal:  IEEE Trans Med Imaging       Date:  1993       Impact factor: 10.048

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5.  High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis.

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Journal:  Magn Reson Med       Date:  1996-11       Impact factor: 4.668

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Journal:  Biochem Pharmacol       Date:  1987-06-15       Impact factor: 5.858

7.  Measurement of regional cerebral glucose utilization with fluorine-18-FDG and PET in heterogeneous tissues: theoretical considerations and practical procedure.

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Journal:  J Nucl Med       Date:  1993-03       Impact factor: 10.057

8.  Spectral analysis of dynamic PET studies.

Authors:  V J Cunningham; T Jones
Journal:  J Cereb Blood Flow Metab       Date:  1993-01       Impact factor: 6.200

9.  Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data.

Authors:  C S Patlak; R G Blasberg; J D Fenstermacher
Journal:  J Cereb Blood Flow Metab       Date:  1983-03       Impact factor: 6.200

10.  Nonlinear model for capillary-tissue oxygen transport and metabolism.

Authors:  Z Li; T Yipintsoi; J B Bassingthwaighte
Journal:  Ann Biomed Eng       Date:  1997 Jul-Aug       Impact factor: 3.934

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  17 in total

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Authors:  Finbarr O'Sullivan; Mark Muzi; David A Mankoff; Janet F Eary; Alexander M Spence; Kenneth A Krohn
Journal:  Ann Appl Stat       Date:  2014-06-01       Impact factor: 2.083

Review 2.  Brain fuel metabolism, aging, and Alzheimer's disease.

Authors:  Stephen Cunnane; Scott Nugent; Maggie Roy; Alexandre Courchesne-Loyer; Etienne Croteau; Sébastien Tremblay; Alex Castellano; Fabien Pifferi; Christian Bocti; Nancy Paquet; Hadi Begdouri; M'hamed Bentourkia; Eric Turcotte; Michèle Allard; Pascale Barberger-Gateau; Tamas Fulop; Stanley I Rapoport
Journal:  Nutrition       Date:  2010-10-29       Impact factor: 4.008

Review 3.  Importance of quantification for the analysis of PET data in oncology: review of current methods and trends for the future.

Authors:  Giampaolo Tomasi; Federico Turkheimer; Eric Aboagye
Journal:  Mol Imaging Biol       Date:  2012-04       Impact factor: 3.488

4.  Model-free quantification of dynamic PET data using nonparametric deconvolution.

Authors:  Francesca Zanderigo; Ramin V Parsey; R Todd Ogden
Journal:  J Cereb Blood Flow Metab       Date:  2015-04-15       Impact factor: 6.200

Review 5.  Quantitative assessment of dynamic PET imaging data in cancer imaging.

Authors:  Mark Muzi; Finbarr O'Sullivan; David A Mankoff; Robert K Doot; Larry A Pierce; Brenda F Kurland; Hannah M Linden; Paul E Kinahan
Journal:  Magn Reson Imaging       Date:  2012-07-21       Impact factor: 2.546

6.  Functional Data Analysis of Dynamic PET Data.

Authors:  Yakuan Chen; Jeff Goldsmith; R Todd Ogden
Journal:  J Am Stat Assoc       Date:  2018-10-26       Impact factor: 5.033

7.  Principles of Tracer Kinetic Analysis in Oncology, Part I: Principles and Overview of Methodology.

Authors:  Austin R Pantel; Varsha Viswanath; Mark Muzi; Robert K Doot; David A Mankoff
Journal:  J Nucl Med       Date:  2022-03       Impact factor: 10.057

8.  An analysis of whole body tracer kinetics in dynamic PET studies with application to image-based blood input function extraction.

Authors:  Jian Huang; Finbarr O'Sullivan
Journal:  IEEE Trans Med Imaging       Date:  2014-05       Impact factor: 10.048

9.  Kinetic Analysis of Dynamic Positron Emission Tomography Data using Open-Source Image Processing and Statistical Inference Tools.

Authors:  David Hawe; Francisco R Hernández Fernández; Liam O'Suilleabháin; Jian Huang; Eric Wolsztynski; Finbarr O'Sullivan
Journal:  Wiley Interdiscip Rev Comput Stat       Date:  2012-02-10

Review 10.  Analysis of Four-Dimensional Data for Total Body PET Imaging.

Authors:  Varsha Viswanath; Rhea Chitalia; Austin R Pantel; Joel S Karp; David A Mankoff
Journal:  PET Clin       Date:  2021-01
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