dcemri.map {dcemri}R Documentation

Pharmacokinetic Modeling of Dynamic Contrast-Enhanced MRI Data

Description

Maximum-a-posteriori (MAP) estimation for single compartment models is performed using literature-based or user-specified arterial input functions.

Usage

dcemri.map(conc, time, img.mask, model="extended", aif=NULL,
             user=NULL, tau.ktrans=1, tau.kep=tau.ktrans,
             ab.vp=c(1,19), ab.tauepsilon=c(1,1/1000),
             samples=FALSE, multicore=FALSE, verbose=FALSE, ...)
dcemri.map.single(conc, time, posterior, parameter, transform, start, hyper, aif)

Arguments

conc Matrix or array of concentration time series (last dimension must be time).
time Time in minutes.
img.mask Mask matrix or array. Voxels with mask=0 will be excluded.
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”
    Weinmann model extended with additional vascular compartment (default)
aif is a character string that identifies the parameters of the type of arterial input function (AIF) used with the above model. Acceptable values are: tofts.kermode (default) or fritz.hansen for the weinmann and extended models; orton.exp (default) or user for the orton.exp model.
user Vector of AIF parameters. For Tofts and Kermode: a_1, m_1, a_2, m_2; for Orton et al.: A_b, μ_b, A_g, μ_g.
tau.ktrans Variance parameter for prior on log(K^{trans}).
tau.kep Variance parameter for prior on log(k_{ep}).
ab.vp Hyper-prior parameters for the Beta prior on vp.
ab.tauepsilon Hyper-prior parameters for observation error Gamma prior.
samples If TRUE output includes samples drawn from the posterior distribution for all parameters.
multicore If TRUE algorithm is parallelized using multicore.
verbose
...
posterior
parameter
transform
start
hyper

aif

Details

Implements maximum-a-posteriori (MAP) estimation for the Bayesian model in Schmid et al. (2006).

Value

Parameter estimates and their standard errors are provided for the masked region of the multidimensional array. They include

ktrans Transfer rate from plasma to the extracellular, extravascular space (EES).
kep Rate parameter for transport from the EES to plasma.
ve Fractional occupancy by EES (the ratio between ktrans and kep).
vp Fractional occupancy by plasma.
sigma2 The residual sum-of-squares from the model fit.
time Acquisition times (for plotting purposes).

Note, not all parameters are available under all models choices.

Author(s)

Volker Schmid

References

Schmid, V., Whitcher, B., Padhani, A.R., Taylor, N.J. and Yang, G.-Z. (2006) Bayesian methods for pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging, IEEE Transactions on Medical Imaging, 25 (12), 1627-1636.

See Also

dcemri.lm, dcemri.bayes

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]

## MAP estimation with Fritz-Hansen default AIF
fit.map <- dcemri.map(img, time, mask, aif="fritz.hansen",
                      nriters=5000)

plot(breast$ktrans, fit.map$ktrans, xlim=c(0,1), ylim=c(0,1),
     xlab=expression(paste("True ", K^{trans})),
     ylab=expression(paste("Estimated ", K^{trans}, " (MAP)")))
abline(0, 1, lwd=1.5, col=2)

## Not run: 
fit.lm <- dcemri.lm(img, time, mask, aif="fritz.hansen")

plot(breast$ktrans, fit.map$ktrans, xlim=c(0,1), ylim=c(0,1),
     xlab=expression(paste("True ", K^{trans})),
     ylab=expression(paste("Estimated ", K^{trans})))
points(breast$ktrans, fit.lm$ktrans, pch=3)
abline(0, 1, lwd=1.5, col="red")
legend("bottomright", c("MAP Estimation (fritz-hansen)",
                        "Levenburg-Marquardt (fritz.hansen)", pch=c(1,3))
## End(Not run)

[Package dcemri version 0.10.5 Index]