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.
1 2 3 4 5 |
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 |
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. |
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 |
multicore |
If |
verbose |
|
... |
|
posterior |
|
parameter |
|
transform |
|
start |
|
hyper |
aif
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. 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
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 | 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 Fritz-Hansen default AIF
fit.map <- dcemri.map(img, time, mask, aif="fritz.hansen",
nriters=5000)
plot(breast$ktrans, fit.map$ktrans, xlim=c(0,1), ylim=c(0,1),
xlab=expression(paste("True ", K^{trans})),
ylab=expression(paste("Estimated ", K^{trans}, " (MAP)")))
abline(0, 1, lwd=1.5, col=2)
## Not run:
fit.lm <- dcemri.lm(img, time, mask, aif="fritz.hansen")
plot(breast$ktrans, fit.map$ktrans, xlim=c(0,1), ylim=c(0,1),
xlab=expression(paste("True ", K^{trans})),
ylab=expression(paste("Estimated ", K^{trans})))
points(breast$ktrans, fit.lm$ktrans, pch=3)
abline(0, 1, lwd=1.5, col="red")
legend("bottomright", c("MAP Estimation (fritz-hansen)",
"Levenburg-Marquardt (fritz.hansen)", pch=c(1,3))
## End(Not run)
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