dcemriS4 is a package, written in the R programming environment, for the quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) for oncology applications. It has been released under the BSD license.
Install the R package using the following commands on the R console
install.packages("devtools")
devtools::install_github("bjw34032/dcemriS4")
library(dcemriS4)
or using NeuroConductor
source("https://neuroconductor.org/neurocLite.R")
# Default Install
neuro_install('dcemriS4')
# from GitHub
neuro_install('dcemriS4', release = "stable", release_repo = "github")
neuro_install('dcemriS4', release = "current", release_repo = "github")
At this point in time the package is not available on CRAN.
The scientific backbone of this software is based on research in the area of parameter estimation and statistical inference. Three scientific publications outline the major techniques available:
- Schmid, 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.
- Schmid, Whitcher, et al. (2009), 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, Whitcher, et al. (2009), 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.
An application of both the Bayesian and frequentist methods to fit compartmental models to DCE-MRI data from a clinical trial may be found at
- Whitcher, Schmid, et al. (2011), A Bayesian Hierarchical Model for DCE-MRI to Evaluate Treatment Response in a Phase II Study in Advanced Squamous Cell Carcinoma of the Head and Neck, Magnetic Resonance Materials in Physics, Biology and Medicine, 24 (2), 85-96.
The dcemriS4 package provides a comprehensive set of R functions that perform all the necessary tasks to produce quantitative estimates of tumor perfusion/permeability using "standard" kinetic models. Data must be in one of two standard formats, Analyze or NIfTI, and results may be written out to either format. If your data are in DICOM format, we suggest using oro.dicom in R or feel free to use one of the many OSS implementations that convert to Analyze or NIfTI.