title: Robust fitting for neuroreceptor mapping.
abstract: Among many other uses, positron emission tomography (PET) can be used in studies to estimate the density of a neuroreceptor at each location throughout the brain by measuring the concentration of a radiotracer over time and modeling its kinetics. There are a variety of kinetic models in common usage and these typically rely on nonlinear least-squares (LS) algorithms for parameter estimation. However, PET data often contain artifacts (such as uncorrected head motion) and so the assumptions on which the LS methods are based may be violated. Quantile regression (QR) provides a robust alternative to LS methods and has been used successfully in many applications. We consider fitting various kinetic models to PET data using QR and study the relative performance of the methods via simulation. A data adaptive method for choosing between LS and QR is proposed and the performance of this method is also studied.
authors.affiliation: Department of Mathematical Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ 07102-1982, USA. cchang@njit.edu
authors.forename: Chung
authors.initials: C
authors.lastname: Chang
authors.forename: R Todd
authors.initials: RT
authors.lastname: Ogden
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging; D006801:Humans; D015233:Models, Statistical; D049268:Positron-Emission Tomography / Q000706:statistics & numerical data; D011984:Sensory Receptor Cells / Q000000981:diagnostic imaging*