Quantitative analysis of dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging for oncology in R

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.