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.
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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 |
model |
is a character string that identifies the type of compartmental model to be used. Acceptable models include:
|
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: |
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 |
multicore |
If |
verbose |
Logical variable (default = |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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])
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