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Tracer Kinetic Modeling: Basics and Concepts

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Basic Sciences of Nuclear Medicine

Abstract

In nuclear medicine studies, such as PET or SPECT, the tracer distribution changes over time depending on delivery, retention and clearance of the tracer in different organs or tissues. If dynamic data acquisition is performed, it is possible to analyse the kinetic behaviour of the tracer and determine quantitative parameters related to various physiological or biochemical processes. This type of information can be clinically relevant in several areas, such as cardiology, oncology and neurology. Mathematical models for the tracer behaviour can be used for estimating outcome measures such as flood flow, volume of distribution or binding potential. Usually an arterial input function is needed, which can be obtained by arterial sampling or sometimes directly from the dynamic PET or SPECT data. For some tracers, an indirect input function, obtained from a reference region, can be used. Simplified, model-free analysis techniques are also available. In summary, kinetic analysis requires defining an acquisition protocol, selecting an appropriate model and analysis technique, as well as the outcome measures desired.

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Notes

  1. 1.

    Parameter identifiability means that a change in the parameter values should always lead to a change in the output function [16].

  2. 2.

    No statistically significant difference.

References

  1. Cunningham VJ, Gunn RN, Matthews JC. Quantification in positron emission tomography for research in pharmacology and drug development. Nucl Med Commun. 2004;25:643–6.

    Article  CAS  PubMed  Google Scholar 

  2. Laruelle M. The role of model-based methods in the development of single scan techniques. Nucl Med Biol. 2000;27:637–42.

    Article  CAS  PubMed  Google Scholar 

  3. Erlandsson K, Buvat I, Pretorius PH, Thomas BA, Hutton BF. A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys Med Biol. 2012;57:R119–59.

    Article  PubMed  Google Scholar 

  4. Carson RE. The development and application of mathematical models in nuclear medicine. J Nucl Med. 1991;32:2206–8.

    CAS  PubMed  Google Scholar 

  5. Carson RE. Tracer kinetic modelling in PET. In: Valk PE, Bailey DL, Townsend DW, Maisey MN, editors. Positron emission tomography: basic science and clinical practice. London: Springer-Verlag; 2003. p. 147–79.

    Google Scholar 

  6. Gunn RN, Gunn SR, Cunningham VJ. Positron emission tomography compartmental models. J Cereb Blood Flow Metab. 2001;21:635–52.

    Article  CAS  PubMed  Google Scholar 

  7. Arfken G. Mathematical methods for physicists. San Diego: Academic; 1985.

    Google Scholar 

  8. Kety SS, Schmidt CF. The nitrous oxide method for the quantitative determination of cerebral blood flow in man: theory, procedure and normal values. J Clin Invest. 1948;27:476–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kety SS. The theory and applications of the exchange of inert gas at the lungs and tissues. Pharmacol Rev. 1951;3:1–41.

    CAS  PubMed  Google Scholar 

  10. Renkin EM. Transport of potassium-42 from blood to tissue in isolated mammalian skeletal muscles. Am J Phys. 1959;197:1205–10.

    Article  CAS  Google Scholar 

  11. Crone C. The permeability of capillaries in various organs as determined by use of the ‘indicator diffusion’ method. Acta Physiol Scand. 1963;58:292–305.

    Article  CAS  PubMed  Google Scholar 

  12. Kerwin RW, Pilowsky LS. Traditional receptor theory and its application to neuroreceptor measurements in functional imaging. Eur J Nucl Med. 1995;22:699–710.

    Article  CAS  PubMed  Google Scholar 

  13. Michaelis L, Menten ML. Die kinetik der invertinwirkung. Biochem Z. 1913;49:1333.

    Google Scholar 

  14. Scatchard G. The attractions of proteins for small molecules and ions. Ann N Y Acad Sci. 1949;51:660–5.

    Article  CAS  Google Scholar 

  15. 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;15:217–27.

    Article  CAS  PubMed  Google Scholar 

  16. Scheibe PO. Identifiability analysis of second-order systems. Nucl Med Biol. 2003;30:827–32.

    Article  PubMed  Google Scholar 

  17. Koeppe RA, Holthoff VA, Frey KA, Kilbourn MR, Kuhl DE. Compartmental analysis of [11C]flumazenil kinetics for the estimation of ligand transport rate and receptor distribution using positron emission tomography. J Cereb Blood Flow Metab. 1991;11:735–44.

    Article  CAS  PubMed  Google Scholar 

  18. Erlandsson K, Bressan RA, Mulligan RS, Gunn RN, Cunningham VJ, Owens J, Wyper D, Ell PJ, Pilowsky LS. Kinetic modelling of [123I]-CNS 1261—a novel SPET tracer for the NMDA receptor. Nucl Med Biol. 2003;30:441–54.

    Article  CAS  PubMed  Google Scholar 

  19. Tonietto M, Rizzo G, Veronese M, Fujita M, Zoghbi SS, Zanotti-Fregonara P, Bertoldo A. Plasma radiometabolite correction in dynamic PET studies: insights on the available modeling approaches. J Cereb Blood Flow Metab. 2016;36:326–39.

    Article  CAS  PubMed  Google Scholar 

  20. Zanotti-Fregonara P, Fadaili EM, Maroy R, Comtat C, Souloumiac A, Jan S, Ribeiro M-J, Gaura V, Bar-Hen A, Trebossen R. Comparison of eight methods for the estimation of the image-derived input function in dynamic [18F]-FDG PET human brain studies. J Cereb Blood Flow Metab. 2009;29:1825–35.

    Article  PubMed  Google Scholar 

  21. Sari H, Erlandsson K, Law I, Larsson HB, Ourselin S, Arridge S, Atkinson D, Hutton BF. Estimation of an image derived input function with MR-defined carotid arteries in FDG-PET human studies using a novel partial volume correction method. J Cereb Blood Flow Metab. 2017;37:1398–409.

    Article  PubMed  Google Scholar 

  22. Innis RB, Cunningham VJ, Delforge J, Fujita M, Gjedde A, Gunn RN, Holden J, Houle S, Huang SC, Ichise M, Iida H, Ito H, Kimura Y, Koeppe RA, Knudsen GM, Knuuti J, Lammertsma AA, Laruelle M, Logan J, Maguire RP, Mintun MA, Morris ED, Parsey R, Price JC, Slifstein M, Sossi V, Suhara T, Votaw JR, Wong DF, Carson RE. Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab. 2007;27:1533–9.

    Article  CAS  PubMed  Google Scholar 

  23. Marquardt DW. An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math. 1963;11:431–41.

    Article  Google Scholar 

  24. Press WH, Teukolsky SA, Vetterling WT, Flannery BP. Numerical recipies in C: the art of scientific computing. Cambridge: Cambridge University Press; 1992.

    Google Scholar 

  25. Feng D, Wong K-P, Wu C-M, Siu W-C. A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: theory and simulation study. IEEE Trans Inform Technol Biomed. 1997;1:243–54.

    Article  CAS  Google Scholar 

  26. Cunningham VJ, Hume SP, Price GR, Ahier RG, Cremer JE, Jones AK. Compartmental analysis of diprenorphine binding to opiate receptors in the rat in vivo and its comparison with equilibrium data in vitro. J Cereb Blood Flow Metab. 1991;11:1–9.

    Article  CAS  PubMed  Google Scholar 

  27. Lammertsma AA, Bench CJ, Hume SP, Osman S, Gunn K, Brooks DJ, Frackowiak RS. Comparison of methods for analysis of clinical [11C]raclopride studies. J Cereb Blood Flow Metab. 1996;16:42–52.

    Article  CAS  PubMed  Google Scholar 

  28. Lammertsma AA, Hume SP. Simplified reference tissue model for PET receptor studies. NeuroImage. 1996;4:153–8.

    Article  CAS  PubMed  Google Scholar 

  29. Wu Y, Carson RE. Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging. J Cereb Blood Flow Metab. 2002;22:1440–52.

    Article  PubMed  Google Scholar 

  30. Erlandsson K, Sivananthan T, Lui D, Spezzi A, Townsend CE, Mu S, Lucas R, Warrington S, Ell PJ. Measuring SSRI occupancy of SERT using the novel tracer [123I]ADAM: a SPECT validation study. Eur J Nucl Med Mol Imaging. 2005;32:1329–36.

    Article  CAS  PubMed  Google Scholar 

  31. Cunningham VJ, Jones T. Spectral analysis of dynamic PET studies. J Cereb Blood Flow Metab. 1993;13:15–23.

    Article  CAS  PubMed  Google Scholar 

  32. Gunn RN, Gunn SR, Turkheimer FE, Aston JAD, Cunningham VJ. Positron emission tomography compartmental models: a basis pursuit strategy for kinetic modelling. J Cereb Blood Flow Metab. 2002;22:1425–39.

    Article  CAS  PubMed  Google Scholar 

  33. Logan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ, MacGregor RR, Hitzemann R, Bendriem B, Gatley SJ, et al. Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C-methyl]-(−)-cocaine PET studies in human subjects. J Cereb Blood Flow Metab. 1990;10:740–7.

    Article  CAS  PubMed  Google Scholar 

  34. Slifstein M, Laruelle M. Effects of statistical noise on graphic analysis of PET neuroreceptor studies. J Nucl Med. 2000;41:2083–8.

    CAS  PubMed  Google Scholar 

  35. Ogden RT. Estimation of kinetic parameters in graphical analysis of PET imaging data. Stat Med. 2003;22:3557–68.

    Article  PubMed  Google Scholar 

  36. Logan J, Fowler JS, Volkow ND, Wang GJ, Ding YS, Alexoff DL. Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab. 1996;16:834–40.

    Article  CAS  PubMed  Google Scholar 

  37. Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983;3:1–7.

    Article  CAS  PubMed  Google Scholar 

  38. Rahmim A, Lodge MA, Karakatsanis NA, Panin VY, Zhou Y, McMillan A, Cho S, Zaidi H, Casey ME, Wahl RL. Dynamic whole-body PET imaging: principles, potentials and applications. Eur J Nucl Med Mol Imaging. 2019;46:501–18.

    Article  PubMed  Google Scholar 

  39. Carson RE. PET physiological measurements using constant infusion. Nucl Med Biol. 2000;27:657–60.

    Article  CAS  PubMed  Google Scholar 

  40. Carson RE, Channing MA, Blasberg RG, Dunn BB, Cohen RM, Rice KC, Herscovitch P. Comparison of bolus and infusion methods for receptor quantitation: application to [18F]cyclofoxy and positron emission tomography. J Cereb Blood Flow Metab. 1993;13:24–42.

    Article  CAS  PubMed  Google Scholar 

  41. Kawai R, Carson RE, Dunn B, Newman AH, Rice KC, Blasberg RG. Regional brain measurement of Bmax and KD with the opiate antagonist cyclofoxy: equilibrium studies in the conscious rat. J Cereb Blood Flow Metab. 1991;11:529–44.

    Article  CAS  PubMed  Google Scholar 

  42. Holden JE, Jivan S, Ruth TJ, Doudet DJ. In vivo receptor assay with multiple ligand concentrations: an equilibrium approach. J Cereb Blood Flow Metab. 2002;22:1132–41.

    Article  CAS  PubMed  Google Scholar 

  43. Bressan RA, Erlandsson K, Mulligan RS, Gunn RN, Cunningham VJ, Owens J, Cullum ID, Ell PJ, Pilowsky LS. A bolus/infusion paradigm for the novel NMDA receptor SPET tracer [123I]CNS 1261. Nucl Med Biol. 2004;31:155–64.

    Article  CAS  PubMed  Google Scholar 

  44. Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr. 1974;19:716–23.

    Article  Google Scholar 

  45. Cunningham VJ. Non-linear regression techniques in data analysis. Med Inf (Lond). 1985;10:137–42.

    CAS  Google Scholar 

  46. Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6:461–4.

    Article  Google Scholar 

  47. Ogden RT, Ojha A, Erlandsson K, Oquendo MA, Mann JJ, Parsey RV. In vivo quantification of serotonin transporters using [11C]DASB and positron emission tomography in humans: modeling considerations. J Cereb Blood Flow Metab. 2007;27:205–17.

    Article  CAS  PubMed  Google Scholar 

  48. Pilowsky LS, Costa DC, Ell PJ, Murray RM, Verhoeff NP, Kerwin RW. Clozapine, single photon emission tomography, and the D2 dopamine receptor blockade hypothesis of schizophrenia. Lancet. 1992;340:199–202.

    Article  CAS  PubMed  Google Scholar 

  49. Travis MJ, Busatto GF, Pilowsky LS, Mulligan R, Acton PD, Gacinovic S, Mertens J, Terriere D, Costa DC, Ell PJ, Kerwin RW. 5-HT2A receptor blockade in patients with schizophrenia treated with risperidone or clozapine. A SPET study using the novel 5-HT2A ligand 123I-5-I-R-91150. Br J Psychiatry. 1998;173:236–41.

    Article  CAS  PubMed  Google Scholar 

  50. Pilowsky LS, Mulligan RS, Acton PD, Ell PJ, Costa DC, Kerwin RW. Limbic selectivity of clozapine. Lancet. 1997;350:490–1.

    Article  CAS  PubMed  Google Scholar 

  51. Erlandsson K, Bressan RA, Mulligan RS, Ell PJ, Cunningham VJ, Pilowsky LS. Analysis of D2 dopamine receptor occupancy with quantitative SPET using the high-affinity ligand [123I]epidepride: resolving conflicting findings. NeuroImage. 2003;19:1205–14.

    Article  PubMed  Google Scholar 

  52. Stone JM, Davis JM, Leucht S, Pilowsky LS. Cortical dopamine D2/D3 receptors are a common site of action for antipsychotic drugs--an original patient data meta-analysis of the SPECT and PET in vivo receptor imaging literature. Schizophr Bull. 2009;35(4):789–97.

    Article  PubMed  Google Scholar 

  53. Ichise M, Meyer JH, Yonekura Y. An introduction to PET and SPECT neuroreceptor quantification models. J Nucl Med. 2001;42:755–63.

    CAS  PubMed  Google Scholar 

  54. 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.

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Kjell Erlandsson .

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Appendices

Appendix 1: Compartmental Models

Expressions for the impulse response functions for the 1-TC and 2-TC models are derived below. L{·} represents the Laplace transform, Laplace domain functions are identified with a tilde, and s is a complex Laplace domain variable.

1.1 1-TC Model

$$ {\displaystyle \begin{array}{c}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{\mathrm{T}}(t)={K}_1{C}_{\mathrm{p}}(t)-{k}_2^{{\prime\prime} }{C}_{\mathrm{T}}(t)\\ {}\iff \\ {}L\left\{\frac{\mathrm{d}}{\mathrm{d}t}{C}_{\mathrm{T}}(t)\right\}=L\left\{{K}_1{C}_{\mathrm{p}}(t)-{k}_2^{{\prime\prime} }{C}_{\mathrm{T}}(t)\right\}\\ {}\iff \\ {}s{\hat{C}}_{\mathrm{T}}(s)-{C}_{\mathrm{T}}(0)={K}_1{\hat{C}}_{\mathrm{p}}(s)-{k}_2^{{\prime\prime} }{\hat{C}}_{\mathrm{T}}(s)\\ {}\Rightarrow \end{array}} $$

With initial condition, CT(0) = 0:

$$ {\displaystyle \begin{array}{c}{\hat{C}}_{\mathrm{T}}(s)=\frac{K_1}{s+{k}_2^{{\prime\prime} }}{\hat{C}}_{\mathrm{p}}(s)\\ {}\iff \\ {}{C}_{\mathrm{T}}(t)={K}_1{\mathrm{e}}^{-{k}_2^{{\prime\prime} }t}\otimes {C}_{\mathrm{p}}(t)\\ {}\iff \end{array}} $$
(20.24)

Impulse response function:

$$ {H}_1(t)={K}_1{\mathrm{e}}^{-{k}_2^{{\prime\prime}}\;t} $$
(20.25)

1.2 2-TC Model

$$ {\displaystyle \begin{array}{c}\begin{array}{l}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{ND}(t)={K}_1{C}_{\mathrm{p}}(t)-\left({k}_2^{\prime }+{k}_3^{\prime}\right){C}_{ND}(t)+{k}_4{C}_{\mathrm{S}}(t)\\ {}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{\mathrm{S}}(t)={k}_3^{\prime }{C}_{ND}(t)-{k}_4{C}_{\mathrm{S}}(t)\end{array}\\ {}\iff \\ {}\left\{\begin{array}{l}s{\hat{C}}_{ND}(s)-{C}_{ND}(0)={K}_1{\hat{C}}_{\mathrm{p}}(s)-\left({k}_2^{\prime }+{k}_3^{\prime}\right){\hat{C}}_{ND}(s)+{k}_4{\hat{C}}_{\mathrm{S}}(s)\\ {}s{\hat{C}}_{\mathrm{S}}(s)-{C}_{\mathrm{S}}(0)={k}_3^{\prime }{\hat{C}}_{ND}(s)-{k}_4{\hat{C}}_{\mathrm{S}}(s)\end{array}\right.\end{array}} $$

With initial conditions, CND(0) = CS(0) = 0:

$$ {\displaystyle \begin{array}{c}\Rightarrow \\ {}\left\{\begin{array}{l}\left(s+{k}_2^{\prime }+{k}_3^{\prime}\right){\hat{C}}_{ND}(s)={K}_1{\hat{C}}_{\mathrm{p}}(s)+{k}_4{\hat{C}}_{\mathrm{S}}(s)\\ {}\left(s+{k}_4\right){\hat{C}}_{\mathrm{S}}(s)={k}_3^{\prime }{\hat{C}}_{ND}(s)\end{array}\right.\end{array}} $$
$$ {\displaystyle \begin{array}{c}\iff \\ {}\left\{\begin{array}{l}{\hat{C}}_{ND}(s)={\left(s+{k}_2^{\prime }+{k}_3^{\prime }-\frac{k_3^{\prime }{k}_4}{s+{k}_4}\right)}^{-1}{K}_1{\hat{C}}_{\mathrm{p}}(s)\\ {}{\hat{C}}_{\mathrm{S}}(s)=\frac{k_3^{\prime }}{s+{k}_4}{\hat{C}}_{ND}(s)\end{array}\right.\end{array}} $$
$$ {\displaystyle \begin{array}{c}\Rightarrow \\ {}{\hat{C}}_T(s)\equiv {\hat{C}}_{ND}(s)+{\hat{C}}_{\mathrm{S}}(s)=\left(1+\frac{k_3^{\prime }}{s+{k}_4}\right){\hat{C}}_{ND}(s)\\ {}=\left(\frac{s+{k}_3^{\prime }+{k}_4}{s+{k}_4}\right)\left(\frac{s+{k}_4}{\left(s+{k}_2^{\prime }+{k}_3^{\prime}\right)\left(s+{k}_4\right)-{k}_3^{\prime }{k}_4}\right){K}_1{\hat{C}}_{\mathrm{p}}(s)\end{array}} $$
$$ {\displaystyle \begin{array}{c}=\left(\frac{s+{k}_3^{\prime }+{k}_4}{s^2+\left({k}_2^{\prime }+{k}_3^{\prime }+{k}_4\right)s+{k}_2^{\prime }{k}_4}\right){K}_1{\hat{C}}_{\mathrm{p}}(s)\\ {}\sim \sim \sim \end{array}} $$
(20.26)

Find poles:

$$ {\displaystyle \begin{array}{c}{s}^2+\left({k}_2^{\prime }+{k}_3^{\prime }+{k}_4\right)s+{k}_2^{\prime }{k}_4=0\\ {}\iff \\ {}s=-\frac{1}{2}\left({k}_2^{\prime }+{k}_3^{\prime }+{k}_4\mp \sqrt{{\left({k}_2^{\prime }+{k}_3^{\prime }+{k}_4\right)}^2-4{k}_2^{\prime }{k}_4}\right)\equiv -{\theta}_{1,2}\\ {}\sim \sim \sim \end{array}} $$

Partial fraction expansion:

$$ \frac{\phi_1}{s+{\theta}_1}+\frac{\phi_2}{s+{\theta}_2}={K}_1\frac{s+{k}_3^{\prime }+{k}_4}{s^2+\left({k}_2^{\prime }+{k}_3^{\prime }+{k}_4\right)s+{k}_2^{\prime }{k}_4} $$
(20.27)
$$ {\displaystyle \begin{array}{c}\iff \\ {}\left\{\begin{array}{l}{\phi}_1={K}_1\frac{k_3^{\prime }+{k}_4-{\theta}_1}{\theta_2-{\theta}_1}\\ {}{\phi}_2=-{K}_1\frac{k_3^{\prime }+{k}_4-{\theta}_2}{\theta_2-{\theta}_1}\end{array}\right.\end{array}} $$
$$ {\displaystyle \begin{array}{c}\sim \sim \sim \\ {}(20.26)+(20.27)\\ {}\Rightarrow \end{array}} $$
$$ {\hat{C}}_{\mathrm{T}}(s)=\left(\frac{\phi_1}{s+{\theta}_1}+\frac{\phi_2}{s+{\theta}_2}\right){\hat{C}}_{\mathrm{p}}(s) $$
(20.28)
$$ {\displaystyle \begin{array}{c}\iff \\ {}{C}_{\mathrm{T}}(t)=\left({\phi}_1{\mathrm{e}}^{-{\theta}_1t}+{\phi}_2{\mathrm{e}}^{-{\theta}_2t}\right)\otimes {C}_{\mathrm{p}}(t)\\ {}\iff \end{array}} $$

Impulse response function:

$$ {H}_2(t)={\phi}_1{\mathrm{e}}^{-{\theta}_1t}+{\phi}_2{\mathrm{e}}^{-{\theta}_2t} $$
(20.29)

Appendix 2: Reference Tissue Models

1.1 1-TC Model

$$ {\displaystyle \begin{array}{c}\left\{\begin{array}{l}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{\mathrm{T}}(t)={K}_1{C}_{\mathrm{p}}(t)-{k}_2^{{\prime\prime} }{C}_{\mathrm{T}}(t)\\ {}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{\mathrm{R}}(t){=}^R{K}_1{C}_{\mathrm{p}}(t){-}^R{k}_2^{\prime }{C}_{\mathrm{R}}(t)\end{array}\right.\\ {}\iff \end{array}} $$

From Eq. (20.24):

$$ {\displaystyle \begin{array}{c}\left\{\begin{array}{l}{\hat{C}}_{\mathrm{T}}(s)=\frac{K_1}{s+{k}_2^{{\prime\prime} }}{\hat{C}}_{\mathrm{p}}(s)\\ {}{\hat{C}}_{\mathrm{R}}(s)=\frac{{}^R{K}_1}{s{+}^R{k}_2^{\prime }}{\hat{C}}_{\mathrm{p}}(s)\end{array}\right.\\ {}\Rightarrow \end{array}} $$

\( \left(\mathrm{with}\;{R}_1=\frac{K_1}{{}^R{K}_1}\right) \):

$$ {\displaystyle \begin{array}{c}{\hat{C}}_{\mathrm{T}}(s)={R}_1\frac{s{+}^R{k}_2^{\prime }}{s+{k}_2^{{\prime\prime} }}{\hat{C}}_{\mathrm{R}}(s)\\ {}={R}_1{\hat{C}}_{\mathrm{R}}(s)+{R}_1\frac{{}^R{k}_2^{\prime }-{k}_2^{{\prime\prime} }}{s+{k}_2^{{\prime\prime} }}{\hat{C}}_{\mathrm{R}}(s)\\ {}\Rightarrow \\ {}{C}_{\mathrm{T}}(t)={R}_1{C}_{\mathrm{R}}(t)+{R}_1\left({}^R{k}_2^{\prime }-{k}_2^{{\prime\prime}}\right){\mathrm{e}}^{-{k}_2^{{\prime\prime} }t}\otimes {C}_{\mathrm{R}}(t)\\ {}\iff \end{array}} $$

Impulse response function:

$$ {H}_{1\mathrm{R}}(t)={R}_1\delta (t)+{R}_1\left({}^R{k}_2^{\prime }-{k}_2^{{\prime\prime}}\right){\mathrm{e}}^{-{k}_2^{{\prime\prime} }t} $$
(20.30)

where δ(t) is the Dirac delta-function.

1.2 2-TC model

$$ \left\{\begin{array}{l}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{ND}(t)={K}_1{C}_{\mathrm{p}}(t)-\left({k}_2^{\prime }+{k}_3^{\prime}\right){C}_{ND}(t)+{k}_4{C}_{\mathrm{S}}(t)\\ {}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{\mathrm{S}}(t)={k}_3^{\prime }{C}_{ND}(t)-{k}_4{C}_{\mathrm{S}}(t)\\ {}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{\mathrm{R}}(t){=}^R{K}_1{C}_{\mathrm{p}}(t){-}^R{k}_2^{\prime }{C}_{\mathrm{R}}(t)\end{array}\right. $$

From Eqs. (20.24) and (20.28):

$$ {\displaystyle \begin{array}{c}\left\{\begin{array}{l}{\hat{C}}_{\mathrm{T}}(s)=\left(\frac{\phi_1}{s+{\theta}_1}+\frac{\phi_2}{s+{\theta}_2}\right){\hat{C}}_{\mathrm{p}}(s)\\ {}{\hat{C}}_{\mathrm{R}}(s)=\frac{{}^R{K}_1}{s{+}^R{k}_2^{\prime }}{\hat{C}}_{\mathrm{p}}(s)\end{array}\right.\\ {}\Rightarrow \\ {}{\hat{C}}_{\mathrm{T}}(s)=\left(\frac{\phi_1}{s+{\theta}_1}+\frac{\phi_2}{s+{\theta}_2}\right)\frac{s{+}^R{k}_2^{\prime }}{{}^R{K}_1}{\hat{C}}_{\mathrm{R}}(s)\\ {}\iff \end{array}} $$

\( \left(\mathrm{with}\;{R}_1=\frac{K_1}{{}^R{K}_1}\right) \):

$$ {\displaystyle \begin{array}{c}{\hat{C}}_{\mathrm{T}}(s)=\frac{R_1}{\theta_2-{\theta}_1}\left(\frac{k_3^{\prime }+{k}_4-{\theta}_1}{s+{\theta}_1}-\frac{k_3^{\prime }+{k}_4-{\theta}_2}{s+{\theta}_2}\right)\left(s{+}^R{k}_2^{\prime}\right){\hat{C}}_{\mathrm{R}}(s)\\ {}=\frac{R_1}{\theta_2-{\theta}_1}\left(\left({k}_3^{\prime }+{k}_4-{\theta}_1\right)\left(1+\frac{{}^R{k}_2^{\prime }-{\theta}_1}{s+{\theta}_1}\right)-\left({k}_3^{\prime }+{k}_4-{\theta}_2\right)\left(1+\frac{{}^R{k}_2^{\prime }-{\theta}_2}{s+{\theta}_2}\right)\right){\hat{C}}_{\mathrm{R}}(s)\\ {}={R}_1{\hat{C}}_{\mathrm{R}}(s)+\frac{R_1}{\theta_2-{\theta}_1}\left(\frac{\left({k}_3^{\prime }+{k}_4-{\theta}_1\right)\left({}^R{k}_2^{\prime }-{\theta}_1\right)}{s+{\theta}_1}-\frac{\left({k}_3^{\prime }+{k}_4-{\theta}_2\right)\left({}^R{k}_2^{\prime }-{\theta}_2\right)}{s+{\theta}_2}\right){\hat{C}}_{\mathrm{R}}(s)\\ {}\iff \end{array}} $$
$$ {\hat{C}}_{\mathrm{T}}(s)={R}_1{\hat{C}}_{\mathrm{R}}(s)+\left(\frac{\rho_1}{s+{\theta}_1}+\frac{\rho_2}{s+{\theta}_2}\right){\hat{C}}_{\mathrm{R}}(s) $$
(20.31)

where

$$ \left\{\begin{array}{l}{\rho}_1={R}_1\frac{\left({k}_3^{\prime }+{k}_4-{\theta}_1\right)\left({}^R{k}_2^{\prime }-{\theta}_1\right)}{\theta_2-{\theta}_1}\\ {}{\rho}_2=-{R}_1\frac{\left({k}_3^{\prime }+{k}_4-{\theta}_2\right)\left({}^R{k}_2^{\prime }-{\theta}_2\right)}{\theta_2-{\theta}_1}\end{array}\right. $$
$$ {\displaystyle \begin{array}{c}\sim \sim \sim \\ {}(20.31)\\ {}\Rightarrow \\ {}{C}_{\mathrm{T}}(t)={R}_1{C}_{\mathrm{R}}(t)+\left({\rho}_1{\mathrm{e}}^{-{\theta}_1t}+{\rho}_2{\mathrm{e}}^{-{\theta}_2t}\right)\otimes {C}_{\mathrm{R}}(t)\\ {}\iff \end{array}} $$

Impulse response function:

$$ {H}_{2\mathrm{R}}(t)={R}_1\delta (t)+{\rho}_1{\mathrm{e}}^{-{\theta}_1t}+{\rho}_2{\mathrm{e}}^{-{\theta}_2t} $$
(20.32)

Appendix 3: Logan Graphical Analysis

1.1 1-TC Model

$$ {\displaystyle \begin{array}{c}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{\mathrm{T}}(t)={K}_1{C}_{\mathrm{p}}(t)-{k}_2^{{\prime\prime} }{C}_{\mathrm{T}}(t)\\ {}\iff \\ {}{C}_{\mathrm{T}}(t)-{C}_{\mathrm{T}}(0)={K}_1{\int}_0^t{C}_{\mathrm{p}}\left(\tau \right)\mathrm{d}\tau -{k}_2^{{\prime\prime} }{\int}_0^t{C}_{\mathrm{T}}\left(\tau \right)\mathrm{d}\tau \end{array}} $$

with CT(0) = 0:

$$ {\displaystyle \begin{array}{c}\iff \\ {}\frac{\int_0^t{C}_{\mathrm{T}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}=\frac{K_1}{k_2^{{\prime\prime} }}\frac{\int_0^t{C}_{\mathrm{p}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}-\frac{1}{k_2^{{\prime\prime} }}\\ {}\Rightarrow \end{array}} $$
$$ \frac{\int_0^t{C}_{\mathrm{T}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}={V}_{\mathrm{T}}\frac{\int_0^t{C}_{\mathrm{p}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}+\mathrm{const}. $$
(20.33)

1.2 2-TC Model

$$ {\displaystyle \begin{array}{c}\left\{\begin{array}{l}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{ND}(t)={K}_1{C}_{\mathrm{p}}(t)-\left({k}_2^{\prime }+{k}_3^{\prime}\right){C}_{ND}(t)+{k}_4{C}_{\mathrm{S}}(t)\\ {}\frac{\mathrm{d}}{\mathrm{d}t}{C}_{\mathrm{S}}(t)={k}_3^{\prime }{C}_{ND}(t)-{k}_4{C}_{\mathrm{S}}(t)\end{array}\right.\\ {}\iff \\ {}\left\{\begin{array}{l}{C}_{ND}(t)-{C}_{ND}(0)={K}_1{\int}_0^t{C}_{\mathrm{p}}\left(\tau \right)\mathrm{d}\tau -\left({k}_2^{\prime }+{k}_3^{\prime}\right){\int}_0^t{C}_{ND}\left(\tau \right) d\tau +{k}_4{\int}_0^t{C}_{\mathrm{S}}\left(\tau \right) d\tau \\ {}{C}_{\mathrm{S}}(t)-{C}_{\mathrm{S}}(0)={k}_3^{\prime }{\int}_0^t{C}_{ND}(t)\mathrm{d}\tau -{k}_4{\int}_0^t{C}_{\mathrm{S}}\left(\tau \right)\mathrm{d}\tau \end{array}\right.\end{array}} $$

with CND(0) = CS(0) = 0:

$$ {\displaystyle \begin{array}{c}\iff \\ {}\left\{\begin{array}{l}{C}_{\mathrm{T}}(t)={K}_1{\int}_0^t{C}_{\mathrm{p}}\left(\tau \right)\mathrm{d}\tau -{k}_2^{\prime }{\int}_0^t{C}_{ND}\left(\tau \right)\mathrm{d}\tau \\ {}{C}_{\mathrm{S}}(t)={k}_3^{\prime }{\int}_0^t{C}_{ND}\left(\tau \right)\mathrm{d}\tau -{k}_4{\int}_0^t{C}_{\mathrm{S}}\left(\tau \right)\mathrm{d}\tau \end{array}\right.\end{array}} $$
$$ {\displaystyle \begin{array}{c}\iff \\ {}\left\{\begin{array}{l}\frac{\int_0^t{C}_{ND}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}=\frac{K_1}{k_2^{\prime }}\frac{\int_0^t{C}_{\mathrm{P}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}-\frac{1}{k_2^{\prime }}\\ {}\frac{\int_0^t{C}_{\mathrm{S}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}=\frac{k_3^{\prime }}{k_4}\frac{\int_0^t{C}_{ND}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}-\frac{1}{k_4}\frac{C_{\mathrm{S}}(t)}{C_{\mathrm{T}}(t)}\end{array}\right.\end{array}} $$
$$ {\displaystyle \begin{array}{c}\Rightarrow \\ {}\frac{\int_0^t{C}_{\mathrm{T}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}\equiv \frac{\int_0^t{C}_{ND}\left(\tau \right)+{C}_{\mathrm{S}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}=\frac{K_1}{k_2^{\prime }}\frac{\int_0^t{C}_{\mathrm{P}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}-\frac{1}{k_2^{\prime }}+\frac{k_3^{\prime }}{k_4}\left(\frac{K_1}{k_2^{\prime }}\frac{\int_0^t{C}_{\mathrm{P}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}-\frac{1}{k_2^{\prime }}\right)-\frac{1}{k_4}\frac{C_{\mathrm{S}}(t)}{C_{\mathrm{T}}(t)}\\ {}=\frac{K_1}{k_2^{\prime }}\left(1+\frac{k_3^{\prime }}{k_4}\right)\frac{\int_0^t{C}_{\mathrm{P}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}-\frac{1}{k_2^{\prime }}\left(1+\frac{k_3^{\prime }}{k_4}\right)-\frac{1}{k_4}\frac{C_{\mathrm{S}}(t)}{C_{\mathrm{T}}(t)}\end{array}} $$
$$ {\displaystyle \begin{array}{c}\iff \\ {}\frac{\int_0^t{C}_{\mathrm{T}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}={V}_{\mathrm{T}}\frac{\int_0^t{C}_{\mathrm{P}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}-\frac{1}{k_2^{{\prime\prime} }}-\frac{1}{k_4}\frac{C_{\mathrm{S}}(t)}{C_{\mathrm{T}}(t)}\\ {}\Rightarrow \end{array}} $$

with CS(t)/CT(t) = constant (pseudo-equilibrium):

$$ \frac{\int_0^t{C}_{\mathrm{T}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}={V}_{\mathrm{T}}\frac{\int_0^t{C}_{\mathrm{P}}\left(\tau \right)\mathrm{d}\tau }{C_{\mathrm{T}}(t)}+\mathrm{const}. $$
(20.34)

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Erlandsson, K. (2021). Tracer Kinetic Modeling: Basics and Concepts. In: Khalil, M.M. (eds) Basic Sciences of Nuclear Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-65245-6_20

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