dce_bayes: Bayesian Methods for Pharmacokinetic Modeling of Dynamic...
In dcemriS4: A Package for Medical Image Analysis (S4 implementation)

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

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

## S4 method for signature 'array'
dcemri.bayes(conc, time, img.mask, model="extended",
             aif=NULL, user=NULL, nriters=3500, thin=3, burnin=500,
             tune=267, ab.ktrans=c(0,1), ab.kep=ab.ktrans,
             ab.vp=c(1,19), ab.tauepsilon=c(1,1/1000),
             samples=FALSE, multicore=FALSE, verbose=FALSE, dic=FALSE,
             ...)
dcemri.bayes.single(conc, time, nriters=3500, thin=3,
                    burnin=500, tune=267, ab.gamma=c(0,1),
                    ab.theta=c(0,1), 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 “kety.orton.exp”Kety 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` and `kety.orton.exp` model.
`user`	Vector of AIF parameters. For Tofts and Kermode: a1, m1, a2, m2; for Orton et al.: Ab, mub, Ag, mug.
`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.
`ab.ktrans`	Mean and variance parameter for Gaussian prior on log(Ktrans).
`ab.kep`	Mean and variance parameter for Gaussian prior on log(kep).
`ab.vp`	Hyper-prior parameters for the Beta prior on vp.
`ab.gamma`	...
`ab.theta`	...
`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`	Logical variable (default = `FALSE`) that allows text-based feedback during execution of the function.
`dic`	If `TRUE`, the deviance information criterion (DIC) and effective number of parameters (pD) will be computed. If “samples = TRUE”, then samples of the DIC and pD will be given.
`aif.model`	...
`aif.parameter`	...
`vp`	...
`...`	Additional parameters to the function.

See Schmid et al. (2006) for details.

Parameter estimates and their standard errors are provided for the masked region of the multidimensional array. All multi-dimensional arrays are output in nifti format.

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).
`DIC`	Deviance information criterion.
`DIC.map`	Contribution to DIC per voxel.
`pD`	Effective number of parameters.
`pD.map`	Constribution to pD per voxel.

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

Volker Schmid volkerschmid@users.sourceforge.net

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")

## 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)

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=2, col=2)
legend("right", c("extended/fritz.hansen","orton.exp/user"), pch=1:2)
cbind(breast$ktrans, fit.bayes$ktrans[,,1], fit.bayes.aif$ktrans[,,1])

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=2, col=2)
legend("bottomright", c("Bayesian Estimation (fritz-hansen)",
                        "Bayesian Estimation (orton.exp)",
                        "Levenburg-Marquardt (weinmann/fritz.hansen)",
                        "Levenburg-Marquardt (orton.exp/user)"), pch=1:4)