Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R | Whitcher | Journal of Statistical Software
Authors: Brandon Whitcher, Volker J. Schmid
Title: Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R
Abstract: The package dcemriS4 provides a complete set of data analysis tools for quantitative assessment of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Image processing is provided for the ANALYZE and NIfTI data formats as input with all parameter estimates being output in NIfTI format. Estimation of T1 relaxation from multiple flip-angle acquisitions, using either constant or spatially-varying flip angles, is performed via nonlinear regression. Both literature-based and data-driven arterial input functions are available and may be combined with a variety of compartmental models. Kinetic parameters are obtained from nonlinear regression, Bayesian estimation via Markov chain Monte Carlo or Bayesian maximum a posteriori estimation. A non-parametric model, using penalized splines, is also available to characterize the contrast agent concentration time curves. Estimation of the apparent diffusion coefficient (ADC) is provided for diffusion-weighted imaging. Given the size of multi-dimensional data sets commonly acquired in imaging studies, care has been taken to maximize computational efficiency and minimize memory usage. All methods are illustrated using both simulated and real-world medical imaging data available in the public domain.

Page views:: 3793. Submitted: 2010-10-20. Published: 2011-10-27.
Paper: Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R     Download PDF (Downloads: 3773)
Supplements:
dcemriS4_0.45.tar.gz: R source package Download (Downloads: 802; 4MB)
v44i05.R: R example code from the paper Download (Downloads: 812; 16KB)
v44i05-data.zip: Example data Download (Downloads: 913; 69MB)
DOI: 10.18637/jss.v044.i05

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.