dce_spline: Bayesian P-Splines for Dynamic Contrasat-Enhanced MRI Data

In dcemri: A Package for Medical Image Analysis

Description

Quantitative analysis of DCE-MRI typically involves the convolution of an arterial input function (AIF) with a nonlinear pharmacokinetic model of the contrast agent concentration. This function takes a semi-parametric penalized spline smoothing approach, with which the AIF is convolved with a set of B-splines to produce a design matrix using locally adaptive smoothing parameters based on Bayesian penalized spline models (P-splines).

Usage

dcemri.spline(conc, time, img.mask, time.input=time,
              model="weinmann", aif="tofts.kermode", user=NULL,
              aif.observed=NULL, nriters=500, thin=5,
              burnin=100, ab.hyper=c(1e-5,1e-5),
              ab.tauepsilon=c(1,1/1000), k=4, p=25, rw=2,
              knots=NULL, nlr=FALSE, t0.compute=FALSE,
              samples=FALSE, multicore=FALSE, verbose=FALSE, ...) 
dcemri.spline.single(conc, time, D, time.input, p, rw, knots, k,
                     A, t0.compute=FALSE, nlr=FALSE, nriters=500,
                     thin=5, burnin=100, ab.hyper=c(1e-5,1e-5),
                     ab.tauepsilon=c(1,1/1000), silent=0,
                     multicore=FALSE, model=NULL,
                     model.func=NULL, model.guess=NULL,
                     samples=FALSE, B=NULL)

Arguments

conc	An array of Gd concentration
time	Time points of aquisition of Gd concentration
img.mask	Array of voxels to fit
time.input	Time points of observed Arterial Gd concentration, defaults to time
model	The type of compartmental model to be used. Acceptable models include: “AATH” or “weinmann” (default).
aif	Arterial input function to use. Values include: “tofts.kermode”, “fritz.hansen” or “observed”. If “observed” you must provide the observed concentrations in aif.observed.
aif.observed	Arterial concentrations observed at timepoints time.input
multicore	Use multicore library
verbose
nlr	Return the generated samples
user
ab.hyper
ab.tauepsilon
p
t0.compute
samples
k
knots
rw
nriters
thin
burnin
D
B
A
silent
model.func
model.guess
...

Details

See Schmid et al. (2009) for more details.

Value

To be added.

Author(s)

Volker Schmid

References

Schmid, V., Whitcher, B., Padhani, A.R. and G.-Z. Yang (2009) A semi-parametric technique for the quantitative analysis of dynamic contrast-enhanced MR images based on Bayesian P-splines, IEEE Transactions on Medical Imaging, 28 (6), 789-798.

See Also

dcemri.bayes, dcemri.lm, dcemri.map

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]

## Generate AIF params using the orton.exp function from Buckley's AIF
aif <- buckley$input[xi]

fit.spline <- dcemri.spline(img, time, mask, aif="fritz.hansen",
                            nriters=250, nlr=TRUE)
fit.spline.aif <- dcemri.spline(img, time, mask, aif="observed",
                                aif.observed=aif, nriters=250,
                                nlr=TRUE)

plot(breast$ktrans, fit.spline$ktrans, xlim=c(0,1), ylim=c(0,1),
     xlab=expression(paste("True ", K^{trans})),
     ylab=expression(paste("Estimated ", K^{trans})))
points(breast$ktrans, fit.spline.aif$ktrans, pch=2)
abline(0, 1, lwd=1.5, col="red")
legend("right", c("fritz.hansen", "observed"), pch=1:2)

cbind(breast$ktrans, fit.spline$ktrans[,,1], fit.spline.aif$ktrans[,,1])

dcemri documentation built on May 2, 2019, 5:27 p.m.