Optimal Metabolite Curve Fitting for Kinetic Modeling of ¹¹C-WAY-100635

¹ New York City Department of Health and Mental Hygiene, New York, New York
² Department of Biostatistics, Columbia University School of Public Health, New York, New York; Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York; Department of Neuroscience, New York State Psychiatric Institute, New York, New York
³ Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York; Department of Neuroscience, New York State Psychiatric Institute, New York, New York; Department of Radiology, Columbia University College of Physicians and Surgeons, New York, New York
⁴ Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York; Department of Neuroscience, New York State Psychiatric Institute, New York, New York

^* To whom correspondence should be addressed. E-mail: to166{at}columbia.edu.

	Abstract

Many quantitative imaging protocols that make use of a metabolite-corrected arterial input function require the use of a mathematic model to describe the rate of metabolism of the radioligand. Commonly, parametric models are fit to metabolism data and then the fitted model is used to correct the plasma input function. ¹¹C-WAY 100635 is a rapidly metabolized radioligand used extensively in mapping the 5-hydroxytryptamine receptor 1A system. Methods: To evaluate the adequacy of fit of 4 metabolite models, we examined data from 92 subjects who received an injection of ¹¹C-WAY 100635, were imaged with PET, and underwent measurement of total plasma concentration and metabolites. The performance of these models was assessed according to residual plots, as well as fit and information criteria. Results: The study showed that the choice of model has a substantial effect on the resulting estimates of outcome measures. Conclusion: Among the models considered, the Hill model provides the best fit across all criteria.

Key Words: nonlinear modeling, residual analysis, kinetic modeling

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