books

Modelling literature

Researchers in Turku PET Centre: ask Vesa Oikonen where to find these.

Introduction to PET modelling

Easy 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.

Carson RE (2005: Tracer Kinetic Modeling in PET. In: Positron Emission Tomography. (Eds: Bailey DL, Townsend DW, Valk PE, Maisey MN) Springer, London, 127-159.

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.

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.

Varnäs K, Varrone A, Farde L. Modeling of PET data in CNS drug discovery and development. J Pharmacokinet Pharmacodyn. 2013; 40(3): 267-279.

Zaidi H (ed.): Quantitative Analysis in Nuclear Medicine Imaging. Springer, 2006. doi: 10.1007/b107410.

More advanced

Blomqvist G, Pauli S, Farde L, Eriksson L, Person A, Halldin C: (1989) Dynamic models of reversible ligand binding. In: Positron Emission Tomography in Clinical Research and Clinical Diagnosis: Tracer Modelling and Radioreceptors (Eds: C Beckers, A Goffinet, A Bol). Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 35-44.

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.

Gjedde A, Wong DF. Mathematical modeling and the quantification of brain dynamics. Neuromethods 2012; 71: 23-39.

Gunn RN, Gunn SR, Turkheimer FE, Aston JAD, Cunningham VJ. Positron emission tomography compartmental models: A basis pursuit strategy for kinetic modeling. J Cereb Blood Flow Metab. 2002; 22: 1425–1439.

Graham MM. Model simplification: complexity versus reduction. Circulation 1985; 72: IV63-IV68.

van den Hoff J. Principles of quantitative positron emission tomography. Amino Acids 2005; 29(4): 341-353.

van den Hoff J. Kinetic Modeling. In: Kiessling F et al. (eds.), Small Animal Imaging, Springer, 2017, p 559-580. doi: 10.1007/978-3-319-42202-2_21.

Ikoma Y, Watabe H, Shidahara, Naganawa M, Kimura Y. PET kinetic analysis: error consideration of quantitative analysis in dynamic studies. Ann Nucl Med. 2008; 22(1): 1-11.

Laruelle M. Imaging synaptic neurotransmission with in vivo binding competition techniques: a critical review. J Cereb Blood Flow Metab. 2000; 20: 423-451.

Laruelle M, Slifstein M, Huang Y. Positron emission tomography: imaging and quantification of neurotransporter availability. Methods 2002; 27:287-299.

Mintun MA, Raichle ME, Kilbourn MR, Wooten GF, Welch MJ. A quantitative model for the in vivo assessment of drug binding sites with positron emission tomography. Ann Neurol. 1984; 15: 217-227. doi: 10.1002/ana.410150302.

Morris ED, Endres CJ, Schmidt KC, Christian BT, Muzic RF Jr, Fisher RE (2004): Kinetic modeling in positron emission tomography. In: Emission Tomography: The Fundamentals of PET and SPECT. (Eds: Wermick MN, Aarsvold JN). Elsevier Inc., pp 499-540.

Price JC. Principles of tracer kinetic analysis. Neuroimag Clin N Am. 2003; 13: 689-704. doi: 10.1016/S1052-5149(03)00107-2.

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 FE, Hinz R, Cunningham VJ. On the undecidability among kinetic models: from model selection to model averaging. J Cereb Blood Flow Metab. 2003; 23: 490-498.

Wong DF, Gjedde A, Wagner HN Jr. Quantification of neuroreceptors in the living human brain. I. Irreversible binding of ligands. J Cereb Blood Flow Metab. 1986; 6: 137-146. doi: 10.1038/jcbfm.1986.27.

Wong DF, Young D, Wilson PD, Meltzer CC, Gjedde A. Quantification of neuroreceptors in the living human brain. III. D2-like dopamine receptors: theory, validation, and changes during normal aging. J Cereb Blood Flow Metab. 1997; 17: 316-330. doi: 10.1097/00004647-199703000-00009.

Very advanced

Cobelli C, Forster D, Toffolo G: (2002) Tracer Kinetics in Biomedical Research: From Data to Model. Kluwer Academic Publishers.

Finkelstein L, Carson ER: (1985) Mathematical Modelling of Dynamic Biological Systems, 2nd ed., Research Studies Press. ISBN: 0-86380-024-6.

Glatting G, Kletting P, Reske SN, Hohl K, Ring C. Choosing the optimal fit function: Comparison of the Akaike information criterion and the F-test. Med Phys. 2007; 34(11): 4285-4292.

Gunn R: (1996) Mathematical modelling and identifiability applied to positron emission tomography data. PhD thesis, University of Warwick.

Gunn RN, Gunn SR, Cunningham VJ. Positron emission tomography compartmental models. J Cereb Blood Flow Metab. 2001; 21: 635–652.

Koeppe RA. Quantitative functional imaging using positron computed tomography and rapid parameter estimation techniques. Thesis (Ph.D.), The Univeristy of Wisconsin, Madison, 1984.

Parsey RV, Slifstein M, Hwang D-R, Abi-Dargham A, Simpson N, Mawlawi O, Guo N-N, Van Heertum R, Mann JJ, Laruelle M. Validation and reproducibility of measurement of 5-HT1A receptor parameters with [carbonyl-11C]WAY-100635 in humans: comparison of arterial and reference tissue input functions. J Cereb Blood Flow Metab 2000; 20: 1111-1133.

Modeling for mathematicians

Lawson RS. Application of mathematical methods in dynamic nuclear medicine studies. Phys Med Biol. 1999; 44: R57-R98.

de Lima JJP. Nuclear medicine and mathematics. Eur J Nucl Med. 1996; 23: 705-719.

Suominen H: Yleistettyyn lokeromallin perustuva spektraalianalyysi positroniemissiotomografia-mallintamisessa. Pro gradu, 2005.

Modeling for biochemists

Alexoff DL, Vaska P, Logan J. Imaging dopamine receptors in the rat striatum with MicroPET R4: kinetic analysis of [11C]raclopride binding using graphical methods. Methods Enzymol. 2004; 385: 213-228.

Dence CS, Herrero P, Schwarz SW, Mach RH, Gropler RJ, Welch MJ. Imaging myocardium enzymatic pathways with carbon-11 radiotracers. Methods Enzymol. 2004; 385: 286-315.

Holden JE, Doudet DJ. Positron emission tomography receptor assay with multiple ligand concentrations: an equilibrium approach. Methods Enzymol. 2004; 385: 169-184.

Morris ED, Christian BT, Yoder KK, Muzic RF Jr. Estimation of local receptor density, B’max, and other parameters via multiple-injection positron emission tomography measurements. Methods Enzymol. 2004; 385: 184-213.

Nikolaus S, Beu M, Vosberg H, Müller H-W, Larisch R. Quantitative analysis of dopamine D2 receptor kinetics with small animal positron emission tomography. Methods Enzymol. 2004; 385: 228-239.

Modeling for preclinical PET

Dupont P, Warwick J. Kinetic modelling in small animal imaging with PET. Methods 2009; 48: 98–103.

Kuntner C. Kinetic modeling in preclinical positron emission tomography. Z Med Phys. 2014 (in press).

Shoghi KI. Quantitative small animal PET. Q J Nucl Mol Imaging 2009; 53: 365-373.

Topping GJ, Dinelle K, Kornelsen R, McCormick S, Holden JE, Sossi V. Positron emission tomography kinetic modeling algorithms for small animal dopaminergic system imaging. Synapse 2010; 64: 200-208.

Zaidi H (ed.): Molecular Imaging of Small Animals - Instrumentation and Applications. Springer Science+Business Media, New York, 2014.

Modeling for clinical medicine

Elgazzar AH. Synopsis of Pathophysiology in Nuclear Medicine. 2014, Springer, ISBN 978-3-319-03457-7.

In Vivo Imaging of Cancer Therapy. Series: Cancer Drug Discovery and Development. Shields AF, Price P (Eds.); 2007, XII, 326 p., Humana Press. ISBN: 978-1-58829-633-7.

Positron Emission Tomography. Basic Sciences. Bailey DL, Townsend DW, Valk PE, Maisey MN (Eds.); 2005, 382 p., Springer. ISBN: 978-1852337988. doi: 10.1007/b136169.

Tomasi G, Turkheimer F, Aboagye E. Importance of quantification for the analysis of PET data in oncology: review of current methods and trends for the future. Mol Imaging Biol. 2012; 14: 131-146. doi: 10.1007/s11307-011-0514-2.

Vriens D, Visser EP, de Geus-Oei L-F, Oyen WJG. Methodological considerations in quantification of oncological FDG PET studies. Eur J Nucl Med Mol Imaging 2010; 37: 1408-1425. doi: 10.1007/s00259-009-1306-7.

Modeling for software developers

Buchert R, van den Hoff J, Mester J. Accurate determination of metabolic rates from dynamic positron emission tomography data with very-low temporal resolution. J Comput Assist Tomography 2003; 27(4): 597-605.

Burger C, Buck A. Requirements and implementation of a flexible kinetic modeling tool. J Nucl Med. 1997; 38: 1818-1823.

Coxson PG, Salmeron EM, Huesman RH, Mazoyer BM. Simulation of compartmental models for kinetic data from a positron emission tomograph. Comput Methods Progr Biomed. 1992; 37: 205-214.

Coxson PG, Huesman RH, Borland L. Consequences of using a simplified kinetic model for dynamic PET data. J Nucl Med. 1997; 38: 660-667.

Chen K, Huang S, Feng D. New estimation methods that directly use the time accumulated counts in the input function in quantitative dynamic PET studies. Phys Med Biol. 1994; 39: 2073-2090.

Hawe D, Fernández FRH, O’Suilleabháin L, Huang J, Wolsztynski E, O’Sullivan F. Kinetic analysis of dynamic positron emission tomography data using open-source image processing and statistical inference tools. WIREs Comput Stat. 2012; 4: 316-322.

Huang S, Bahn MM, Phelps ME. A new technique for solving nonlinear differential equations encountered in modeling of neuroreceptor-binding ligands. IEEE Trans Nucl Sci. 1988; 35(1): 762-766.

Garfinkel D. Computer modeling, complex biological systems, and their simplifications. Am J Physiol. 1980; 239: R1-R6.

Mazoyer BM, Huesman RH, Budinger TF, Knittel BL. Dynamic PET data analysis. J Comput Assist Tomogr. 1986; 10(4): 645-653.

Muzic RF Jr, Cornelius S. COMKAT: compartmental model kinetic analysis tool. J Nucl Med. 2001; 42(4): 636-645.

Phair RD. Development of kinetic models in the nonlinear world of molecular cell biology. Metabolism 1997; 46: 1489-1495.

Veronese M, Rizzo G, Turkheimer FE, Bertoldo A. SAKE: a new quantification tool for positron emission tomography studies. Comput Methods Progr Biomed. 2013; 111: 199-213.


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Created at: 2007-06-06
Updated at: 2018-11-24
Written by: Vesa Oikonen