Quantitative Analysis of DCE-MRI using R

Quantitative Analysis of
Dynamic Contrast-Enhanced MRI using R
The dcemriS4 package
Brandon Whitcher PhD CStat
b.whitcher@imperial.ac.uk
GlaxoSmithKline Clinical Imaging Centre
London, United Kingdom
rigorousanalytics.blogspot.com — www.dcemri.org — dcemri.blogspot.com
@whitcher — @dcemri — @rigorous
20 July 2010 for useR!2010
B. Whitcher (GSK CIC) DCE-MRI in R 20 July 2010 1 / 19

Outline
1 Motivation
2 Dynamic Contrast Enhanced MRI
3 Parameter Estimation
4 Conclusions

Motivation
Introduction
The quantitative analysis of DCE-MRI involves ﬁtting
pharmacokinetic (PK) models to the concentration of a contrast
agent over time.
Gadolinium-based contrast agents, involving a small molecular-weight
substance, are injected after several baseline scans.
Using T1-weighted sequences, the reduction in T1 relaxation time
caused by the contrast agent is the signal dominant enhancement.
T1-weighted kinetic curves have three major phases
the upslope
maximum enhancement
washout
Dynamic acquisition
5-10 minutes for oncology applications
60-90 minutes for neurology (BBB) applications
dcemriS4 facilitates all stages of data analysis for DCE-MRI, and
diﬀusion-weighted imaging (DWI), using S4 nifti objects.

Motivation
RIDER Neuro MRI

Dynamic Contrast Enhanced MRI
Data Acquisition
1 Localizer
2 Structural scans
3 Multiple ﬂip angles
4 B1 characterization (3T or higher ﬁelds)
5 Dynamic (bolus injection + 30s)
6 Structural scans
7 DWI

Data Acquisition
1 Motion correction and co-registration
2 T1 relaxation
3 Gadolinium concentration
4 B1 mapping
For higher ﬁeld strengths (3T or more)
5 Arterial input function
6 Kinetic parameter estimation
7 Statistical inference

Tips and Tricks
Automation is the key to accurate and consistent results
Quantitative analysis of DCE-MRI depends on several key acquisition
parameters
oro.dicom provides the facility of converting DICOM header
information into a CSV ﬁle
path <- file.path(".")
subject <- 1086100996
dcm <- dicomSeparate(file.path(path, subject))
## Save DICOM header information to CSV file
dcm.csv <- dicomTable(dcm$hdr)
write.csv(dcm.csv, file=paste(subject, "csv", sep="."))

T1 Relaxation
Figure: T1 Phantom
Parametric form dictated
by physics
Multiple ﬂip-angle
acquisitions
Nonlinear regression
Levenburg-Marquardt
minpack.lm
B1 ﬁeld correction is
possible

Parameter Estimation
R code for T1 Estimation
R> alpha <- c(5, 10, 20, 25, 15)
R> TR <- 4.22 / 1000 # seconds
R> R1 <- R1.fast(flip, mask, alpha, TR, verbose = TRUE)
Deconstructing data...
Calculating R10 and M0...
Reconstructing results...
R> overlay(vibe, 1/R1$R10[, , 1:nsli(vibe)], z = 13,
+ zlim.x = c(0, 1024), zlim.y = c(0, 2.5),
+ plot.type = "single")
Flip angles in degrees
Repetition time in seconds
Signal intensities = 4D array
Mask = 3D array
Visualization provided by overlay() in oro.nifti

Arterial Input Functions
Cp(t) = D [a1 exp(−m1t) + a2 exp(−m2t)]
Variables
D = 0.1mmol/kg, a1 = 3.99kg/l, a2 = 4.78kg/l , m1 = 0.144min−1
and m2 = 0.0111min−1
Weinmann et al. (1984); Tofts and Kermode (1984).
D = 0.1mmol/kg, a1 = 2.4kg/l, a2 = 0.62kg/l, m1 = 3.0min−1 and
m2 = 0.016min−1
Fritz-Hansen et al. (1996).
Cp(t) = ABt exp(−µBt) + AG [exp(−µG t) − exp(−µBt)] ,
Variables
Orton et al. (2008)
Seed-based algorithm in extract.aif()

Kinetic Parameter Estimation
The standard Kety (1951) model, a single-compartment model, or the
extended Kety model, forms the basis for dcemriS4.
Ct(t) = Ktrans
[Cp(t) ⊗ exp(−kept)]
Ct(t) = vpCp(t) + Ktrans
[Cp(t) ⊗ exp(−kept)]
Estimation techniques include:
Nonlinear regression (Levenburg-Marquardt, minpack.lm)
Bayesian estimation via MCMC (Schmid et al. 2006)
Bayesian estimation via MAP (adaptation of Schmid et al. 2006)
Bayesian estimation via penalized B-splines (Schmid et al. 2009a)
Hierarchical Bayesian methods are not available at this time in
dcemriS4, but will be in PILFER.
http://pilfer.sourceforge.net

Kinetic Parameter Estimation
acqtimes <- str2time(unique(sort(scan("rawtimes.txt", quiet=TRUE))))$time
conc <- readNIfTI(paste(s, d, "perfusion", "gdconc", sep="_"))
mask <- readANALYZE(paste(s, d, "perfusion", "mask2", sep="_"))
fit <- dcemri.lm(conc, (acqtimes - acqtimes[8]) / 60,
ifelse(mask > 0, TRUE, FALSE), model="extended",
aif="fritz.hansen", verbose=TRUE)
writeNIfTI(fit$ktrans, paste(s, d, "perfusion", "ktrans", sep="_"))
overlay(dyn[, ,6:9,],
ifelse(fit$ktrans[, ,6:9] < 0.25, fit$ktrans[, ,6:9], NA),
w=11, zlim.x=c(32,512), col.y=hotmetal(), zlim.y=c(0,.1))
Acquisition times are found in the DICOM data
DICOM header ﬁelds are vendor dependent
Zero must be deﬁned as time of gadolinium injection
Mask was created in FSLView
Visualization provided by overlay() in oro.nifti
Time conversion provided by str2time() in oro.dicom

RIDER Neuro MRI: Ktrans

RIDER Neuro MRI: kep

Statistical Inference
Methodology for statistical inference is not included in the dcemriS4
package.
Please use the models/tests in R to perform hypothesis tests.
Hierarchical Bayesian models are available, but not using R.
See Schmid et al. (2009b) for more information.

Conclusions
Conclusions
The package dcemriS4 attempts to provide quantitative methods for
DCE-MRI
Vendor software (GE, Siemens, Philips, etc.)
Proprietary software (JIM, etc.)
Home-grown solutions
Future directions
Multi-compartment models (Buckley et al.)
Parallelization (e.g., multicore)
Semi-parametric procedures (AUC, Tmax , Cmax , etc.)
Feedback
Please provide feedback (pos/neg) on the SourceForge forum or
mailing list.

Conclusions
References I
Fritz-Hansen, T., E. Rostrup, et al. (1996). Measurement of the arterial
concentration of Gd-DTPA using MRI: A step toward quantitative perfusion
imaging. Magnetic Resonance in Medicine, 36, 225–231.
Kety, S. S. (1951). The theory and applications of the exchange of inert gas
at the lungs and tissues, Pharmaceutical Review, 3, 1–41.
Larsson H. B. W, M. Stubgaard, et al. (1990). Quantitation of blood-brain
barrier defect by magnetic resonance imaging and gadolinium-DTPA in
patients with multiple sclerosis and brain tumors. Magnetic Resonance in
Medicine, 16, 117–131.
Orton M. R., J. A. d’Arcy, et al. (2008). Computationally eﬃcient vascular
input function models for quantitative kinetic modelling using DCE-MRI.
Physics in Medicine and Biology, 53, 1225–1239.
Schmid, V., B. Whitcher, et al. (2006). Bayesian Methods for
Pharmacokinetic Models in Dynamic Contrast-Enhanced Magnetic
Resonance Imaging, IEEE Transactions in Medical Imaging, 25 (12),
1627–1636.

Conclusions
References II
Schmid, V. J., B. Whitcher, et al. (2009a). A Bayesian Hierarchical Model
for the Analysis of a Longitudinal Dynamic Contrast-Enhanced MRI
Oncology Study, Magnetic Resonance in Medicine, 61 (1), 163–174.
Schmid, V. J., B. Whitcher, et al. (2009b). A Semi-parametric Technique
for the Quantitative Analysis of Dynamic Contrast-enhanced MR Images
Based on Bayesian P-splines, IEEE Transactions in Medical Imaging, 28 (6),
789–798.
Tofts P. S., A. G. Kermode (1984). Measurement of the blood-brain barrier
permeability and leakage space using dynamic MR imaging. 1. Fundamental
concepts. Magnetic Resonance in Medicine, 17(2), 357–367.
Weinmann H. J., M. Laniado, W. Mutzel (1984). Pharmacokinetics of
Gd-DTPA/dimeglumine after intraveneous injection into healthy volunteers.
Physiological Chemistry and Physics and Medical NMR, 16, 167–172.

Quantitative Analysis of DCE-MRI using R

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