dcemri.map-methods: Pharmacokinetic Modeling of Dynamic Contrast-Enhanced MRI...
In neuroconductor/dcemriS4: A Package for Image Analysis of DCE-MRI (S4 Implementation)

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, ...)

## S4 method for signature 'array'
dcemri.map(
  conc,
  time,
  img.mask,
  model = "extended",
  aif = NULL,
  user = NULL,
  ab.ktrans = c(0, 1),
  ab.kep = ab.ktrans,
  ab.vp = c(1, 19),
  ab.tauepsilon = c(1, 1/1000),
  maxit = 5000,
  samples = FALSE,
  multicore = FALSE,
  verbose = FALSE,
  ...
)

`conc`	Matrix or array of concentration time series (last dimension must be time).
`...`	Additional parameters to the function.
`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 “orton.cos” Extended model using Orton's raised cosine AIF “kety.orton.cos” Kety model using Orton's raised cosine 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 and `orton.exp` model; `user` for the `orton.cos` model and `orton.cos` 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.
`ab.ktrans`	Mean and variance parameter for Gaussian prior on \log(K^{trans}).
`ab.kep`	Mean and variance parameter for Gaussian 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.
`maxit`	The maximum number of iterations for the optimization procedure.
`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.

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. The multi-dimensional arrays are provided in nifti format.

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 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.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 extended Kety model and Fritz-Hansen default AIF
fit.map.vp <- dcemri.map(img, time, mask, aif="fritz.hansen")
## Nonlinear regression with extended Kety model and Fritz-Hansen default AIF
fit.lm.vp <- dcemri.lm(img, time, mask, aif="fritz.hansen")

plot(breast$ktrans, fit.map.vp$ktrans, xlim=c(0,1), ylim=c(0,1),
     xlab=expression(paste("True ", K^{trans})),
     ylab=expression(paste("Estimated ", K^{trans})))
points(breast$ktrans, fit.lm.vp$ktrans, pch=3)
abline(0, 1, lwd=2, col=2)
legend("bottomright", c("MAP Estimation (fritz.hansen)",
                        "Levenburg-Marquardt (fritz.hansen)"), pch=c(1,3))

## MAP estimation with Kety model and Fritz-Hansen default AIF
fit.map <- dcemri.map(img, time, mask, model="weinmann", aif="fritz.hansen")
## Nonlinear regression with Kety model and Fritz-Hansen default AIF
fit.lm <- dcemri.lm(img, time, mask, model="weinmann", aif="fritz.hansen")

cbind(breast$kep, fit.lm$kep[,,1], fit.lm.vp$kep[,,1], fit.map$kep[,,1],
      fit.map.vp$kep[,,1])
cbind(breast$ktrans, fit.lm$ktrans[,,1], fit.lm.vp$ktrans[,,1],
      fit.map$ktrans[,,1], fit.map.vp$ktrans[,,1])