Literature DB >> 10326723

Which linear compartmental systems can be analyzed by spectral analysis of PET output data summed over all compartments?

K Schmidt1.   

Abstract

General linear time-invariant compartmental systems were examined to determine which systems meet the conditions necessary for application of the spectral analysis technique to the sum of the concentrations in all compartments. Spectral analysis can be used to characterize the reversible and irreversible components of the system and to estimate the minimum number of compartments, but it applies only to systems in which the measured data can be expressed as a positively weighted sum of convolution integrals of the input function with an exponential function that has real-valued nonpositive decay constants. The conditions are met by compartmental systems that are strongly connected, have exchange of material with the environment confined to a single compartment, and do not contain cycles, i.e., there is no possibility for material to pass from one compartment through two or more compartments back to the initial compartment. Certain noncyclic systems with traps, systems with cycles that obey a specified loop condition, and noninterconnected collections of such systems also meet the conditions. Dynamic positron emission tomographic data obtained after injection of a radiotracer, the kinetics of which can be described by any model in the class of models identified here, can be appropriately analyzed with the spectral analysis technique.

Mesh:

Year:  1999        PMID: 10326723     DOI: 10.1097/00004647-199905000-00010

Source DB:  PubMed          Journal:  J Cereb Blood Flow Metab        ISSN: 0271-678X            Impact factor:   6.200


  10 in total

1.  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

2.  Voxelwise quantification of [(11)C](R)-rolipram PET data: a comparison between model-based and data-driven methods.

Authors:  Gaia Rizzo; Mattia Veronese; Paolo Zanotti-Fregonara; Alessandra Bertoldo
Journal:  J Cereb Blood Flow Metab       Date:  2013-03-20       Impact factor: 6.200

3.  Use of spectral analysis with iterative filter for voxelwise determination of regional rates of cerebral protein synthesis with L-[1-11C]leucine PET.

Authors:  Mattia Veronese; Kathleen C Schmidt; Carolyn Beebe Smith; Alessandra Bertoldo
Journal:  J Cereb Blood Flow Metab       Date:  2012-03-07       Impact factor: 6.200

4.  A spectral analysis approach for determination of regional rates of cerebral protein synthesis with the L-[1-(11)C]leucine PET method.

Authors:  Mattia Veronese; Alessandra Bertoldo; Shrinivas Bishu; Aaron Unterman; Giampaolo Tomasi; Carolyn Beebe Smith; Kathleen C Schmidt
Journal:  J Cereb Blood Flow Metab       Date:  2010-03-03       Impact factor: 6.200

5.  Cumulative input function method for linear compartmental models and spectral analysis in PET.

Authors:  Urban Simoncic; Robert Jeraj
Journal:  J Cereb Blood Flow Metab       Date:  2010-09-01       Impact factor: 6.200

Review 6.  Spectral Analysis of Dynamic PET Studies: A Review of 20 Years of Method Developments and Applications.

Authors:  Mattia Veronese; Gaia Rizzo; Alessandra Bertoldo; Federico E Turkheimer
Journal:  Comput Math Methods Med       Date:  2016-12-05       Impact factor: 2.238

7.  Test-retest reproducibility of quantitative binding measures of [11C]Ro15-4513, a PET ligand for GABAA receptors containing alpha5 subunits.

Authors:  Colm J McGinnity; Daniela A Riaño Barros; Lula Rosso; Mattia Veronese; Gaia Rizzo; Alessandra Bertoldo; Rainer Hinz; Federico E Turkheimer; Matthias J Koepp; Alexander Hammers
Journal:  Neuroimage       Date:  2017-03-11       Impact factor: 6.556

8.  Parametric Mapping for TSPO PET Imaging with Spectral Analysis Impulsive Response Function.

Authors:  Mattia Veronese; Marcello Tuosto; Tiago Reis Marques; Oliver Howes; Belen Pascual; Meixiang Yu; Joseph C Masdeu; Federico Turkheimer; Alessandra Bertoldo; Paolo Zanotti-Fregonara
Journal:  Mol Imaging Biol       Date:  2021-01-21       Impact factor: 3.488

9.  Estimate of FDG excretion by means of compartmental analysis and ant colony optimization of nuclear medicine data.

Authors:  Sara Garbarino; Giacomo Caviglia; Massimo Brignone; Michela Massollo; Gianmario Sambuceti; Michele Piana
Journal:  Comput Math Methods Med       Date:  2013-09-28       Impact factor: 2.238

10.  Test-retest reproducibility of cannabinoid-receptor type 1 availability quantified with the PET ligand [¹¹C]MePPEP.

Authors:  Daniela A Riaño Barros; Colm J McGinnity; Lula Rosso; Rolf A Heckemann; Oliver D Howes; David J Brooks; John S Duncan; Federico E Turkheimer; Matthias J Koepp; Alexander Hammers
Journal:  Neuroimage       Date:  2014-04-13       Impact factor: 6.556

  10 in total

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