Spectral analysis: principle and clinical applications

Murase, Kenya

doi:10.1007/bf03006429

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…King et al 1996; Ostergaard et al 1996). Figures 6 and 7 provide little support for exponentially decaying residence densities or even monotonely decreasing ones that are implied by spectral analysis (Cunningham and Jones 1993; Gunn et al 2002; Murase 2003; Keeling et al 2004). …”

Section: Discussionmentioning

confidence: 94%

“…Even though our experience is that the B-spline approach is satisfactory, more sophisticated approaches might also be considered. In this regard, a general compartmental modeling approach based on approximation of residues by sums of exponentials was proposed by Cunningham and Jones (1993), see also Murase (2003). These techniques, known as spectral analysis in the PET literature, approximate the residence density using a mixture of exponentials…”

Section: Inference For the Residue Functionmentioning

confidence: 99%

“…Suppose there are a full a set of J + 3 B-splines resulting from this. The first three elements have support on the left-most time bin and provide the ability to capture the monotonic decreasing structure corresponding to residence time densities of standard one- or two-compartmental models (Murase 2003). As there is not a compelling reason to retain the corresponding basis elements on the right side, they are not included.…”

Section: Inference For the Residue Functionmentioning

confidence: 99%

“…Thus PET uptake data do not merely represent the local metabolic process that is of interest and so the separation of the plasma time-course from the PET uptake data become essential. The most widely used approach is kinetic analysis with compartmental models (Cunningham, Gunn, and Matthews 2004; Huang and Phelps 1986; Mazoyer, Huesman, Budinger, and Knittel 1986; Mankoff, Muzi, and Zaidi 2005; Murase 2003). Here the metabolic processes in the tissue are represented by a series of compartments within the tissue.…”

Section: Introductionmentioning

confidence: 99%

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

O’Sullivan

Muzi

Spence

et al. 2009

Journal of the American Statistical Association

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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 twocompartment 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.

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Section: Discussionmentioning

confidence: 94%

Section: Inference For the Residue Functionmentioning

confidence: 99%

Section: Inference For the Residue Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

O’Sullivan

Muzi

Spence

et al. 2009

Journal of the American Statistical Association

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“…Although there have been many developments in this direction [e.g., Cunningham and Jones (1993), Murase (2003), O’Sullivan (1993), Muzi et al (2012), Veronese et al (2013)], no procedure has yet been widely adopted for routine use. Most often quantitation of dynamic PET studies is based on consideration of a single time point for a user-defined region of interest (ROI).…”

Section: Introductionmentioning

confidence: 99%

Voxel-level mapping of tracer kinetics in PET studies: A statistical approach emphasizing tissue life tables

et al. 2014

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Most radiotracers used in dynamic positron emission tomography (PET) scanning act in a linear time-invariant fashion so that the measured time-course data are a convolution between the time course of the tracer in the arterial supply and the local tissue impulse response, known as the tissue residue function. In statistical terms the residue is a life table for the transit time of injected radiotracer atoms. The residue provides a description of the tracer kinetic information measurable by a dynamic PET scan. Decomposition of the residue function allows separation of rapid vascular kinetics from slower blood-tissue exchanges and tissue retention. For voxel-level analysis, we propose that residues be modeled by mixtures of nonparametrically derived basis residues obtained by segmentation of the full data volume. Spatial and temporal aspects of diagnostics associated with voxel-level model fitting are emphasized. Illustrative examples, some involving cancer imaging studies, are presented. Data from cerebral PET scanning with 18F fluoro-deoxyglucose (FDG) and 15O water (H2O) in normal subjects is used to evaluate the approach. Cross-validation is used to make regional comparisons between residues estimated using adaptive mixture models with more conventional compartmental modeling techniques. Simulations studies are used to theoretically examine mean square error performance and to explore the benefit of voxel-level analysis when the primary interest is a statistical summary of regional kinetics. The work highlights the contribution that multivariate analysis tools and life-table concepts can make in the recovery of local metabolic information from dynamic PET studies, particularly ones in which the assumptions of compartmental-like models, with residues that are sums of exponentials, might not be certain.

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Tracer Kinetics

Bella¹

2006

Encyclopedia of Medical Devices and Instrumentation

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Spectral analysis: principle and clinical applications

Cited by 9 publications

References 16 publications

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

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

Voxel-level mapping of tracer kinetics in PET studies: A statistical approach emphasizing tissue life tables

Tracer Kinetics

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