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).
1 2 3 4 5 6 7 8 9 10 | aif.orton.exp(tt, AB, muB, AG, muG)
orton.exp.lm(
tt,
aif,
guess = c(log(100), log(10), log(1), log(0.1)),
nprint = 0
)
model.orton.exp(tt, aparams, kparams)
|
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. |
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 |
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.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 |
message |
The text message associated with
the |
Brandon Whitcher bwhitcher@gmail.com
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | 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,]
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.