dce_bayes: Bayesian Methods for Pharmacokinetic Modeling of Dynamic...
In dcemri: A Package for Medical Image Analysis

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

Bayesian analysis of contrast agent concentration time curves from DCE-MRI.

dcemri.bayes(conc, time, img.mask, model="extended", aif=NULL,
             user=NULL, nriters=3500, thin=3, burnin=500,
             tune=267, 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.bayes.single(conc, time, nriters=3500, thin=3,
                    burnin=500, tune=267, tau.gamma=1,
                    tau.theta=1, ab.vp=c(1,19),
                    ab.tauepsilon=c(1,1/1000), aif.model=0,
                    aif.parameter=c(2.4,0.62,3,0.016), vp=1)

`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) “orton.exp”Extended model using Orton's exponential AIF
`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.
`nriters`	Total number of iterations.
`thin`	Thining factor.
`burnin`	Number of iterations for burn-in.
`tune`	Number for iterations for tuning. The algorithm will be tuned to an acceptance rate between 0.3 and 0.6.
`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`
`tau.gamma`
`tau.theta`
`aif.model`
`aif.parameter`
`vp`
`...`

See Schmid et al. (2006) for details.

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).
`ktranserror`	Error on ktrans.
`kep`	Rate parameter for transport from the EES to plasma.
`keperror`	Error on kep.
`ve`	Fractional occupancy by EES (the ratio between ktrans and kep).
`vperror`	Error on ve.
`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.map, dcemri.spline

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]

## Bayesian estimation with Fritz-Hansen default AIF
fit.bayes <- dcemri.bayes(img, time, mask, aif="fritz.hansen", nriters=1500)

## Bayesian estimation with "orton.exp" function fit to Buckley's AIF
aif <- buckley$input[xi]
aifparams <- orton.exp.lm(time, aif)
aifparams$D <- 1
fit.bayes.aif <- dcemri.bayes(img, time, mask, model="orton.exp",
                              aif="user", user=aifparams, nriters=1500)

plot(breast$ktrans, fit.bayes$ktrans, xlim=c(0,1), ylim=c(0,1),
     xlab=expression(paste("True ", K^{trans})),
     ylab=expression(paste("Estimated ", K^{trans}, " (Bayesian)")))
points(breast$ktrans, fit.bayes.aif$ktrans, pch=2)
abline(0, 1, lwd=1.5, col=2)
legend("right", c("orton.exp", "fritz.hansen"), pch=1:2)

cbind(breast$ktrans, fit.bayes$ktrans[,,1], fit.bayes.aif$ktrans[,,1])

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

plot(breast$ktrans, fit.bayes$ktrans, xlim=c(0,1), ylim=c(0,1),
     xlab=expression(paste("True ", K^{trans})),
     ylab=expression(paste("Estimated ", K^{trans})))
points(breast$ktrans, fit.bayes.aif$ktrans, pch=2)
points(breast$ktrans, fit.lm$ktrans, pch=3)
points(breast$ktrans, fit.lm.aif$ktrans, pch=4)
abline(0, 1, lwd=1.5, col="red")
legend("bottomright", c("Bayesian Estimation (fritz-hansen)",
                        "Bayesian Estimation (orton.exp)",
                        "Levenburg-Marquardt (fritz.hansen)",
                        "Levenburg-Marquardt (orton.exp)"), pch=1:4)

## End(Not run)