PET basics II How to get numbers? Modeling for PET Turku PET Centre 2008-04-15 [email protected]

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Transcript of PET basics II How to get numbers? Modeling for PET Turku PET Centre 2008-04-15 [email protected]

  • Slide 1
  • PET basics II How to get numbers? Modeling for PET Turku PET Centre 2008-04-15 [email protected]
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  • PET is quantitative Concentrations as a function of time Bq/mL nCi/cc nmol/L Model Analysis report RegionReceptor occupancy striatum45% putamen43% caudatus 49% frontal34% occipital28% Regional biochemical, physiological and pharmacological parameters per tissue volume Perfusion Glucose consumption Enzyme activity Volume of distribution Binding potential Receptor occupancy...
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  • Modeling for PET Tracer selection Comprehensive model Workable model Model validation Model application Huang & Phelps 1986
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  • Dynamic processes in vivo Translocation Transformation Binding Enzyme
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  • 1. Translocation Delivery and removal by the circulatory system Active and passive transport over membranes Vesicular transport inside cells
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  • 2. Transformation Enzyme-catalyzed reactions: (de)phosphorylation, (de)carboxylation, (de)hydroxylation, (de)hydrogenation, (de)amination, oxidation/reduction, isomerisation Spontaneous reactions Enzyme
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  • 3. Binding Binding to plasma proteins Specific binding to receptors and activation sites Specific binding to DNA and RNA Specific binding between antibody and antigen Non-specific binding
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  • Dynamic processes Dynamic process is of first-order, when its speed depends on one concentration only Standard mathematical methods assume first-order kinetics
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  • First-order kinetics AP k For a first-order process A->P, the velocity v can be expressed as, where k is a first-order rate constant; k is independent of concentration of A and time; its unit is sec -1 or min -1.
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  • Pseudo-first-order kinetics Dynamic processes in PET involve two or more reactants If the concentration of one reactant is very small compared to the others, equations simplify to the same form as for first-order kinetics This is one reason why we use tracer doses in PET (see Appendix 1)
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  • Compartmental model Compartmental model assumes that: injected isotope exists in the body in a fixed number of physical or chemical states (compartments, see appendix 3), with specified interconnections among them; the arrows indicate the possible pathways the tracer can follow (dynamic processes) Compartmental models can be described in terms of a set of linear, first-order, constant- coefficient, ordinary differential equations (ODE)
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  • Compartmental model Change of tracer concentration in one of the compartments is a linear function of the concentrations in all other compartments:
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  • Compartmental model By convention, in the nuclear medicine literature, the first compartment is the blood or plasma pool
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  • One-tissue compartment model Change over time of the tracer concentration in tissue, C 1 (t) : C0C0 C1C1 K1K1 k2k2
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  • Two-tissue compartment model C0C0 C1C1 C2C2 K1K1 k2k2 k3k3 k4k4
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  • Three-tissue compartment model C0C0 C1C1 C2C2 C3C3 K1K1 k2k2 k3k3 k4k4 k5k5 k6k6
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  • Customized compartmental models Perfusion (blood flow) with [ 15 O]H 2 O CACA CTCT f f/p
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  • Customized compartmental models Glucose transport and phosphorylation in skeletal muscle with [ 18 F]FDG CACA C EC C IC CMCM K1K1 k3k3 k4k4 k2k2 k5k5
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  • C A H2O Customized compartmental models Oxygen consumption in skeletal muscle with [ 15 O]O 2 C A O2 C SM O2 + C Mb O2 K1K1 k3k3 k 2 O2 C SM H2O K1K1 k 2 O2
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  • Customized compartmental models Simplified reference tissue model for [ 11 C]raclopride brain studies See appendix 4 CACA C F + C NS + C B K1K1 k2k2 C F + C NS K1K1 k2k2 Cerebellum ROI
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  • ... continued Simplified reference tissue model for [ 11 C]raclopride brain studies
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  • Solving differential equations Linear first-order ordinary differential equation (ODE) can be solved using Laplace transformation; see appendix 5 alternative method; see appendix 6
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  • Applying differential equations Simulation: calculate regional tissue curve based on arterial plasma curve model physiological model parameters
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  • Model fitting Tissue TAC measured using PET is the sum of TACs of tissue compartments and blood in tissue vasculature Simulated PET TAC:
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  • Model fitting Minimization of weighted residual sum-of-squares: Otherwise If measurement variance is known
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  • Model fitting Initial guess of parameters Simulated PET TACMeasured PET TAC Measured plasma TAC Weighted sum-of-squares Final model parameters New guess of parameters Model if too large if small enough
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  • Model comparison More complex model allows always better fit to noisy data Parameter confidence intervals with bootstrapping Significance of the information gain by additional parameters: F test, AIC, SC Alternative to model selection: Model averaging with Akaike weights
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  • Models that are independent on any specific compartment model structure Spectral analysis Multiple-time graphical analysis (MTGA): Gjedde-Patlak Logan (see PET basics I)
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  • Distributed models Distributed models are generally accepted to correspond more closely to physiological reality than simpler compartment models In PET imaging, compartment models have been shown to provide estimates of receptor concentration that are as good as those of a distributed model, and are assumed to be adequate for analysis of PET imaging data in general (Muzic & Saidel, 2003).
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  • How to get numbers in practice? Follow the instructions in quality system: SOP, MET, DAN Check that documentation is not outdated PETO Retrieve data for analysis Record study documentation Store final analysis results
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  • PETO http://petintra/Instructions/PETO_manual.pdf
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  • Using analysis software Can be used on any PC with Windows XP in hospital network and/or PET intranet Downloadable in WWW Analysis instructions in WWW http://www.pet.fi/ or http://www.turkupetcentre.net/ P:\bin\windows
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  • Additional information
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  • Requesting software New software Feature requests Bug reports Project follow-up Software documents http://petintra/softaryhma/ or ask IT or modelling group members
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  • More reading Budinger TF, Huesman RH, Knittel B, Friedland RP, Derenzo SE (1985): Physiological modeling of dynamic measurements of metabolism using positron emission tomography. In: The Metabolism of the Human Brain Studied with Positron Emission Tomography. (Eds: Greitz T et al.) Raven Press, New York, 165-183. Cunningham VJ, Rabiner EA, Matthews JC, Gunn RN, Zamuner S, Gee AD. Kinetic analysis of neuroreceptor binding using PET. Int Congress Series 2004; 1265: 12- 24. van den Hoff J. Principles of quantitative positron emission tomography. Amino Acids 2005; 29(4): 341-353. Huang SC, Phelps ME (1986): Principles of tracer kinetic modeling in positron emission tomography and autoradiography. In: Positron Emission Tomography and Autoradiography: Principles and Applications for the Brain and Heart. (Eds: Phelps,M; Mazziotta,J; Schelbert,H) Raven Press, New York, 287-346. Ichise M, Meyer JH, Yonekura Y. An introduction to PET and SPECT neuroreceptor quantification models. J. Nucl. Med. 2001; 42:755-763. Lammertsma AA, Hume SP. Simplified reference tissue model for PET receptor studies. Neuroimage 1996; 4: 153-158. Lammertsma AA. Radioligand studies: imaging and quantitative analysis. Eur. Neuropsychopharmacol. 2002; 12: 513-516. Laruelle M. Modelling: when and why? Eur. J. Nucl. Med. 1999; 26, 571-572. Laruelle M. Imaging synaptic neurotransmission with in vivo binding competition techniques: a critical review. J. Cereb. Blood Flow Metab. 2000; 20: 423-451.
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  • Even more reading Laruelle M, Slifstein M, Huang Y. Positron emission tomography: imaging and quantification of neurotransporter availability. Methods 2002; 27:287-299. Logan J. Graphical analysis of PET data applied to reversible and irreversible tracers. Nucl. Med. Biol. 2000; 27:661-670. Meikle SR, Eberl S, Iida H. Instrumentation and methodology for quantitative pre- clinical imaging studies. Curr. Pharm. Des. 2001; 7(18): 1945-1966. Passchier J, Gee A, Willemsen A, Vaalburg W, van Waarde A. Measured drug- related receptor occupancy with positron emission tomography. Methods 2002; 27:278-286. Schmidt KC, Turkheimer FE. Kinetic modeling in positron emission tomography. Q. J. Nucl. Med. 2002; 46:70-85. Slifstein M, Laruelle M. Models and methods for derivation of in vivo neuroreceptor parameters with PET and SPECT reversible radiotracers. Nucl. Med. Biol. 2001; 28:595-608. Turkheimer F, Sokoloff L, Bertoldo A, Lucignani G, Reivich M, Jaggi JL, Schmidt K. Estimation of component and parameter distributions in spectral analysis. J. Cereb. Blood Flow Metabol. 1998; 18: 1211-122