Multiple linear analysis methods for the quantification of irreversibly binding radiotracers

Su Jin Kim, Jae Sung Lee, Yu Kyeong Kim, James Frost, Gary Wand, Mary E. McCaul, Dong Soo Lee

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Gjedde-Patlak graphical analysis (GPGA) has commonly been used to quantify the net accumulations (Kin) of radioligands that bind or are taken up irreversibly. We suggest an alternative approach (MLAIR: multiple linear analysis for irreversible radiotracers) for the quantification of these types of tracers. Two multiple linear regression model equations were derived from differential equations of the two-tissue compartment model with irreversible binding. Multiple linear analysis for irreversible radiotracer 1 has a desirable feature for ordinary least square estimations because only the dependent variable CT(t) is noisy. Multiple linear analysis for irreversible radiotracer 2 provides Kin from direct estimates of the coefficients of independent variables without the mediation of a division operation. During computer simulations, MLAIR1 provided less biased Kin estimates than the other linear methods, but showed a high uncertainty level for noisy data, whereas MLAIR2 increased the robustness of estimation in terms of variability, but at the expense of increased bias. For real [11C]MeNTI positron emission tomography data, both methods showed good correlations, with parameters estimated using the standard nonlinear least squares method. Multiple linear analysis for irreversible radiotracer 2 parametric images showed remarkable image quality as compared with GPGA images. It also showed markedly improved statistical power for voxelwise comparisons than GPGA. The two MLAIR approaches examined were found to have several advantages over the conventional GPGA method.

Original languageEnglish (US)
Pages (from-to)1965-1977
Number of pages13
JournalJournal of Cerebral Blood Flow and Metabolism
Issue number12
StatePublished - Dec 2008


  • Neuroreceptor imaging
  • Parametric images
  • Positron emission tomography
  • Tracer kinetic modeling

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine


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