dcemriS4 (version 0.51)

dcemri.map: 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

## S3 method for class '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, ...)

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

Value

• 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.

item

• aif
• user
• ab.ktrans
• ab.kep
• ab.vp
• ab.tauepsilon
• maxit
• samples
• multicore
• verbose
• ...

code

FALSE

eqn

$v_p$

emph

et al.

pkg

multicore

Details

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

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 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])