Description Usage Arguments Details Value Author(s) References See Also Examples
Bayesian analysis of contrast agent concentration time curves from DCE-MRI.
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 | dcemri.bayes(conc, ...)
## S4 method for signature 'array'
dcemri.bayes(
conc,
time,
img.mask,
model = "extended",
spatial = 0,
aif = NULL,
user = NULL,
nriters = 3000,
thin = 3,
burnin = 1000,
tune = 267,
ab.ktrans = if (spatial == 0) c(0, 1) else { c(1e-04, 1e-04) },
ab.kep = ab.ktrans,
ab.vp = c(1, 19),
ab.tauepsilon = c(1, 1/1000),
samples = FALSE,
parallel = FALSE,
verbose = FALSE,
dic = 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:
|
spatial |
is an integer specifying spatial smoothing of kinetic parameters
|
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: 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.tauepsilon |
Hyper-prior parameters for observation error Gamma prior. |
samples |
If |
parallel |
If |
verbose |
Logical variable (default = |
dic |
If |
vp |
Fractional occupancy in the plasma space. |
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
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 29 30 31 32 33 34 35 36 37 38 39 40 | 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",
nriters=1000, thin=2, burnin=200)
## 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,
nriters=1000, thin=2, burnin=200)
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)
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)
|
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