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Kinetic analysis of dynamic positron emission tomography data using open‐source image processing and statistical inference tools

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Abstract In dynamic mode, positron emission tomography (PET) can be used to track the evolution of injected radio‐labelled molecules in living tissue. This is a powerful diagnostic imaging technique that provides a unique opportunity to probe the status of healthy and pathological tissue by examining how it processes substrates. The spatial aspect of PET is well established in the computational statistics literature. This article focuses on its temporal aspect. The interpretation of PET time‐course data is complicated because the measured signal is a combination of vascular delivery and tissue retention effects. If the arterial time‐course is known, the tissue time‐course can typically be expressed in terms of a linear convolution between the arterial time‐course and the tissue residue. In statistical terms, the residue function is essentially a survival function—a familiar life‐time data construct. Kinetic analysis of PET data is concerned with estimation of the residue and associated functionals such as flow, flux, volume of distribution, and transit time summaries. This review emphasizes a nonparametric approach to the estimation of the residue based on a piecewise linear form. Rapid implementation of this by quadratic programming is described. The approach provides a reference for statistical assessment of widely used one‐ and two‐ compartmental model forms. We illustrate the method with data from two of the most well‐established PET radiotracers, 15O‐H2O and 18F‐fluorodeoxyglucose, used for assessment of blood perfusion and glucose metabolism, respectively. The presentation illustrates the use of two open‐source tools, AMIDE and R, for PET scan manipulation and model inference. WIREs Comput Stat 2012, 4:316–322. doi: 10.1002/wics.1196 This article is categorized under: Applications of Computational Statistics > Health and Medical Data/Informatics Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Applications of Computational Statistics > Signal and Image Processing and Coding

One‐Compartment (1C) Model.

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Kinetic analysis of dynamic positron emission tomography (PET) brain studies with 15O‐H2O (top) and 18F‐fluorodeoxyglucose (FDG) (bottom). Left: AMIDE view of the tracer uptake data (colour) together with a coregistered T1‐weighted magnetic resonance (MR) scan for 15O‐H2O and a PET tissue attenuation scan for FDG. The AMIDE region of interests (ROIs) for a grey matter region and tumor region are outlined in orange. Middle: ROI data and nonparametric (NP) fit (grey line). Fitted compartmental and NP residue functions scaled by flow. Right: details of kinetic analysis. The measured arterial input (CP) and the data weighting factor for decay and time binning (fk in section on statistical inference). Boxplots of weighted residuals from compartmental and NP models. Key parameter values (bias‐corrected) with estimated standard errors. Reference histogram (yellow) for improvement in fit of the NP over compartmental model‐observed value (vertical black line).

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Two‐Compartment (2C) Model.

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Applications of Computational Statistics > Signal and Image Processing and Coding
Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
Applications of Computational Statistics > Health and Medical Data/Informatics

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