Modified regression model for the Logan plot

József Varga, Zsolt Szabo

Research output: Contribution to journalArticle

76 Citations (Scopus)

Abstract

Logan's graphical model is a robust estimation of the total distribution volume (DVt) of reversibly bound radio-pharmaceuticals, but the resulting DVt values decrease with increasing noise. The authors hypothesized that the noise dependence can be reduced by a linear regression model that minimizes the sum of squared perpendicular rather than vertical (y) distances between the data points and fitted straight line. To test the new method, 15 levels of simulated noise (repeated 2,000 times) were added to synthetic tissue activity curves, calculated from two different sets of kinetic parameters. Contrary to the traditional method, there was no (P > 0.05) or dramatically decreased noise dependence with the perpendicular model. Real dynamic 11C (+) McN5652 serotonin transporter binding data were processed either by applying Logan analysis to average counts of large areas or by averaging the Logan slopes of individual-voxel data. There were no significant differences between the parameters when the perpendicular regression method was used with both approaches. The presented experiments show that the DVt calculated from the Logan plot is much less noise dependent if the linear regression model accounts for errors in both the x and y variables, allowing fast creation of unbiased parametric images from dynamic positron-emission tomography studies.

Original languageEnglish
Pages (from-to)240-244
Number of pages5
JournalJournal of Cerebral Blood Flow and Metabolism
Volume22
Issue number2
DOIs
Publication statusPublished - Jan 1 2002

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Keywords

  • Graphical analysis
  • Logan plot
  • Positron emission tomography

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine

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