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Kinetic curves from single compartment models are computed from kinetic parameters.

kineticModel(time, par, model = "extended", aif = "fritz.hansen")

Arguments

time

is a vector of acquisition times (in minutes).

par

is a list of kinetic parameters; e.g., list("ktrans"=0.5,"kep"=1).

model

is a character string that identifies the type of compartmental model to be used. Acceptable models include: “weinmann” Tofts & Kermode AIF convolved with single compartment model “extended” (default) Weinmann model extended with additional vascular compartment, ...

aif

is a character string that identifies the type of arterial input function (AIF) to be used. Acceptable AIF models include: tofts.kermode, fritz.hansen

Value

Computed pharmacokinetic curve.

Details

Compartmental models are the solution to the modified general rate equation (Kety 1951). The specific parametric models considered here include the basic Kety model Ct(t)=Ktrans[Cp(t)exp(kept)], where is the convolution operator, and the so-called extended Kety model Ct(t)=vpCp(t)+Ktrans[Cp(t)exp(kept)]. The arterial input function must be literature-based (with fixed parameters).

References

Fritz-Hansen, T., Rostrup, E., Larsson, H.B.W, Sondergaard, L., Ring, P. and Henriksen, O. (1993) Measurement of the arterial concentration of Gd-DTPA using MRI: A step toward quantitative perfusion imaging, Magnetic Resonance in Medicine, 36, 225-231.

Tofts, P.S., Brix, G, Buckley, D.L., Evelhoch, J.L., Henderson, E., Knopp, M.V., Larsson, H.B.W., Lee, T.-Y., Mayr, N.A., Parker, G.J.M., Port, R.E., Taylor, J. and Weiskoff, R. (1999) Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusable tracer: Standardized quantities and symbols, Journal of Magnetic Resonance, 10, 223-232.

Tofts, P.S. and Kermode, A.G. (1984) Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts, Magnetic Resonance in Medicine, 17, 357-367.

Weinmann, H.J., Laniado, M. and Mutzel, W. (1984) Pharmacokinetics of Gd-DTPA/dimeglumine after intraveneous injection into healthy volunteers, Physiological Chemistry and Physics and Medical NMR, 16, 167-172.

See also

Author

Brandon Whitcher bwhitcher@gmail.com and Volker Schmid volkerschmid@users.sourceforge.net

Examples


data("buckley")
xi <- seq(5, 300, by=5)
img <- array(t(breast$data)[,xi], c(13,1,1,60))
mask <- array(TRUE, dim(img)[1:3])
time <- buckley$time.min[xi]

fit.lm <- dcemri.lm(img, time, mask, aif="fritz.hansen")
par.lm <- c("vp"=fit.lm$vp[3], "ktrans"=fit.lm$ktrans[3], "kep"=fit.lm$kep[3])
curve.lm <- kineticModel(time, par.lm)
plot(time, img[3,1,1,], xlab="time", ylab="contrast agent concentration")
lines(time, curve.lm, lwd=2, col=2)

fit.bayes <- dcemri.bayes(img, time, mask, aif="fritz.hansen")
par.bayes <- c("vp"=fit.bayes$vp[3], "ktrans"=fit.bayes$ktrans[3],
               "kep"=fit.bayes$kep[3])
curve.bayes <- kineticModel(time, par.bayes)
lines(time, curve.bayes, lwd=2, col=4)
legend("bottomright", c("Levenburg-Marquardt (extended/fritz.hansen)",
                        "Bayesian Estimation (extended/fritz-hansen)"),
       lwd=2, col=c(2,4))

cbind(time, img[3,,,], curve.lm, curve.bayes)[20:30,]
#>           time           curve.lm curve.bayes
#>  [1,] 1.650000 0.775142 0.7662219   0.7659948
#>  [2,] 1.733333 0.778938 0.7701721   0.7699190
#>  [3,] 1.816667 0.782176 0.7737276   0.7734502
#>  [4,] 1.900000 0.784939 0.7769461   0.7766458
#>  [5,] 1.983333 0.787302 0.7798738   0.7795521
#>  [6,] 2.066667 0.789326 0.7825480   0.7822059
#>  [7,] 2.150000 0.791067 0.7849987   0.7846373
#>  [8,] 2.233333 0.792571 0.7872505   0.7868706
#>  [9,] 2.316667 0.793878 0.7893233   0.7889259
#> [10,] 2.400000 0.795021 0.7912340   0.7908197
#> [11,] 2.483333 0.796029 0.7929963   0.7925659