Literature DB >> 12072847

Kinetic modeling in positron emission tomography.

K C Schmidt1, F E Turkheimer.   

Abstract

Most PET kinetic modeling approaches have at their basis a compartmental model that has first-order, constant coefficients. The present article outlines the one-, two-, and three-compartment models used to measure cerebral blood flow, cerebral glucose metabolism, and receptor binding, respectively. The number of compartments of each model is based on specific knowledge of the physiological and/or biochemical compartments into which the tracer distributes. Additional physical and biochemical properties of the tracer distribution are considered in specifying the use of first-order rate constants. For example, in cerebral blood flow and receptor binding studies transport across the blood-brain barrier by diffusion can be modeled as a first-order process. A saturable carrier-mediated process or saturable enzyme catalyzed reaction, when tracer doses of the labeled substrate are used and the natural substrate is in steady-state, also results in first-order rate constants, as in glucose metabolism studies. The rate of ligand binding, on the other hand, depends on the concentrations of both substrate and available receptors. In order to appropriately model the reaction as pseudo first-order during a specified experimental interval, protocols are carefully designed to assure that the number of available binding sites remains approximately constant throughout the given interval. A broad array of scanning protocols is employed for kinetic analyses. These include single-scan approaches, which function like their autoradiographic counterparts in animal studies and are often called "autoradiographic" methods, which allow estimation of a single parameter. Dynamic scanning to obtain the time course of tissue activity allows simultaneous estimation of multiple parameters. Scanning may be conducted during a period of tracer uptake or after attainment of steady-state conditions. All quantitative modeling approaches share the common requirement that an arterial input function be measured or an appropriate surrogate be found. A vast array of methods is available for estimation of model parameters, both micro and macro. In the final analysis, it is the interaction among all elements of the PET study, including careful tracer selection, model specification, experimental protocol design, and sound parameter estimation methods, that determines the quantitative accuracy of the estimates of the physiological or biochemical process under study.

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Year:  2002        PMID: 12072847

Source DB:  PubMed          Journal:  Q J Nucl Med        ISSN: 1125-0135


  43 in total

1.  Predicting brain occupancy from plasma levels using PET: superiority of combining pharmacokinetics with pharmacodynamics while modeling the relationship.

Authors:  Euitae Kim; Oliver D Howes; Bo-Hyung Kim; Jae Min Jeong; Jae Sung Lee; In-Jin Jang; Sang-Goo Shin; Federico E Turkheimer; Shitij Kapur; Jun Soo Kwon
Journal:  J Cereb Blood Flow Metab       Date:  2011-12-21       Impact factor: 6.200

Review 2.  How to measure drug transport across the blood-brain barrier.

Authors:  Ulrich Bickel
Journal:  NeuroRx       Date:  2005-01

3.  Wavelet denoising for voxel-based compartmental analysis of peripheral benzodiazepine receptors with (18)F-FEDAA1106.

Authors:  Miho Shidahara; Yoko Ikoma; Chie Seki; Yota Fujimura; Mika Naganawa; Hiroshi Ito; Tetsuya Suhara; Iwao Kanno; Yuichi Kimura
Journal:  Eur J Nucl Med Mol Imaging       Date:  2007-11-20       Impact factor: 9.236

Review 4.  The potential of PET/MR for brain imaging.

Authors:  Wolf-Dieter Heiss
Journal:  Eur J Nucl Med Mol Imaging       Date:  2009-03       Impact factor: 9.236

Review 5.  Quantitative Rodent Brain Receptor Imaging.

Authors:  Kristina Herfert; Julia G Mannheim; Laura Kuebler; Sabina Marciano; Mario Amend; Christoph Parl; Hanna Napieczynska; Florian M Maier; Salvador Castaneda Vega; Bernd J Pichler
Journal:  Mol Imaging Biol       Date:  2020-04       Impact factor: 3.488

6.  First in-human PET study and kinetic evaluation of [18F]AS2471907 for imaging 11β-hydroxysteroid dehydrogenase type 1.

Authors:  Shivani Bhatt; Nabeel B Nabulsi; Songye Li; Zhengxin Cai; David Matuskey; Jason Bini; Soheila Najafzadeh; Michael Kapinos; Jim R Ropchan; Richard E Carson; Kelly P Cosgrove; Yiyun Huang; Ansel T Hillmer
Journal:  J Cereb Blood Flow Metab       Date:  2019-03-21       Impact factor: 6.200

7.  Relative Patlak plot for dynamic PET parametric imaging without the need for early-time input function.

Authors:  Yang Zuo; Jinyi Qi; Guobao Wang
Journal:  Phys Med Biol       Date:  2018-08-10       Impact factor: 3.609

8.  Dynamic PET of human liver inflammation: impact of kinetic modeling with optimization-derived dual-blood input function.

Authors:  Guobao Wang; Michael T Corwin; Kristin A Olson; Ramsey D Badawi; Souvik Sarkar
Journal:  Phys Med Biol       Date:  2018-07-24       Impact factor: 3.609

9.  Generalized algorithms for direct reconstruction of parametric images from dynamic PET data.

Authors:  Guobao Wang; Jinyi Qi
Journal:  IEEE Trans Med Imaging       Date:  2009-05-12       Impact factor: 10.048

Review 10.  Imaging and modeling of myocardial metabolism.

Authors:  Sebastian Obrzut; Neema Jamshidi; Afshin Karimi; Ulrika Birgersdotter-Green; Carl Hoh
Journal:  J Cardiovasc Transl Res       Date:  2010-02-25       Impact factor: 4.132

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