dce_map: Pharmacokinetic Modeling of Dynamic Contrast-Enhanced MRI...
In dcemri: A Package for Medical Image Analysis

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

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

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)

`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

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

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.

Volker Schmid

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.

dcemri.lm, dcemri.bayes

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)