Kinetic Modeling in Positron Emission Tomography

Evan D. Morris, Christopher J. Endres, Kathleen C. Schmidt, Bradley T. Christian, Raymond F. Muzic, Ronald E. Fisher

Research output: Chapter in Book/Report/Conference proceedingChapter

52 Scopus citations

Abstract

This chapter focuses on kinetic modeling, which uses mathematical techniques to explain the behavior of tracer compounds in the body and which is capable of summarizing important information about the body's physiology. It examines mathematical kinetic models used to analyze time sequences of positron emission tomography (PET) images to gather quantitative information about the body. This includes methods used in the two most ubiquitous applications of PET namely, imaging of blood flow and glucose metabolism. Furthermore, this chapter also examines the use of PET to image specific receptor molecules, which capitalizes on the unique specificity of PET. Kinetic models for PET typically derive from the one-, two-, or three-compartment model in which a directly measured blood curve serves as the model's input function. The coefficients of the differential equations in the model are taken to be constants that are reflective of inherent kinetic properties of the particular tracer molecule in the system. By formally comparing the output of the model to the experimentally obtained PET data, estimating values for these kinetic parameters is possible and thus extracts information about binding, delivery, or any hypothesized process, as distinct from all other processes contributing to the PET signal.

Original languageEnglish (US)
Title of host publicationEmission Tomography
Subtitle of host publicationThe Fundamentals of PET and SPECT
PublisherElsevier Inc.
Pages499-540
Number of pages42
ISBN (Electronic)9780080521879
ISBN (Print)9780127444826
DOIs
StatePublished - Nov 18 2004
Externally publishedYes

ASJC Scopus subject areas

  • General Medicine
  • General Neuroscience

Fingerprint

Dive into the research topics of 'Kinetic Modeling in Positron Emission Tomography'. Together they form a unique fingerprint.

Cite this