aif: Arterial Input Functions
In dcemriS4: A Package for Medical Image Analysis (S4 implementation)

Description Usage Arguments Details Value Author(s) References See Also Examples

Parametric models for arterial input functions (AIFs) that are compatible with single compartment models for dynamic contrast-enhanced MRI (DCE-MRI).

aif.orton.exp(tt, AB, muB, AG, muG)
model.orton.exp(tt, aparams, kparams)
orton.exp.lm(tt, aif, guess=c(log(100),log(10),log(1),log(0.1)),
             nprint=0)

`tt`	is a vector of acquisition times (in minutes) relative to injection of the contrast agent. Negative values should be used prior to the injection.
`AB,muB,AG,muG`	are parameters of the double exponential function that describe the AIF.
`aparams`	is the vector of parameters (A_B, μ_B, A_G, μ_G) associated with the AIF.
`kparams`	is the vector of parameters (v_p, K^{trans}, k_{ep}) associated with the “extended Kety model” for contrast agent concentration.
`aif`	is the vector of observed contrast agent concentrations (data) used to estimate the parametric model.
`guess`	Initial parameter values for the nonlinear optimization.
`nprint`	is an integer, that enables controlled printing of iterates if it is positive. In this case, estimates of `par` are printed at the beginning of the first iteration and every `nprint` iterations thereafter and immediately prior to return. If `nprint` is not positive, no tracing information on the progress of the optimization is produced.

aif.orton.exp displays the exponential AIF from Orton et al. (2008) for a known set of AIF parameter values. model.orton.exp displays the exponential AIF from Orton et al. (2008) for a known set of AIF and compartmental model parameter values. orton.exp.lm estimates the AIF parameters, using nonlinear optimization, using a vector of observed contrast agent concentrations.

aif.orton.exp and model.orton.exp return the AIF associated with the pre-specified parameter values.

orton.exp.lm returns a list structure with

`AB`	The amplitude of the first exponential function.
`muB`	The decay rate of the first exponential function.
`AG`	The amplitude of the second exponential function.
`muG`	The decay rate of the second exponential function.
`info`	The success (or failure) code from the Levenburg-Marquardt algorithm `nls.lm`.
`message`	The text message associated with the `info` paramters.

Brandon Whitcher bjw34032@users.sourceforge.net

Orton, M.R., Collins, D.J., Walker-Samuel, S., d'Arcy, J.A., Hawkes, D.J., Atkinson, D. and Leach, M.O. (2007) Bayesian estimation of pharmacokinetic parameters for DCE-MRI with a robust treatment of enhancement onset time, Physics in Medicine and Biology 52, 2393-2408.

Orton, M.R., d'Arcy, J.A., Walker-Samuel, S., Hawkes, D.J., Atkinson, D., Collins, D.J. and Leach, M.O. (2008) Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI, Physics in Medicine and Biology 53, 1225-1239.

dcemri.lm, extract.aif, nls.lm

data("buckley")
## Generate AIF params using the orton.exp function from Buckley's AIF
xi <- seq(5, 300, by=5)
time <- buckley$time.min[xi]
aif <- buckley$input[xi]
aifparams <- orton.exp.lm(time, aif)
aifparams$D <- 1 
unlist(aifparams[1:4])

aoe <- aif.orton.exp(time, aifparams$AB, aifparams$muB, aifparams$AG,
                     aifparams$muG)
with(buckley, plot(time.min, input, type="l", lwd=2))
lines(time, aoe, lwd=2, col=2)
legend("right", c("Buckley's AIF", "Our approximation"), lty=1,
       lwd=2, col=1:2)
cbind(time, aif, aoe)[1:10,]