/en/tags/imaging/ imaging Source Themes Academic (https://sourcethemes.com/academic/)en-uk[Impressum](impressum/index.html) – © 2021 · Hosted on [uberspace.de](https://uberspace.de)Fri, 22 Feb 2019 00:00:00 +0000 /img/icon-192.png /en/tags/imaging/ /en/software/dcemri/ Fri, 22 Feb 2019 00:00:00 +0000 /en/software/dcemri/ <p>R packages for working with DCE-MRI data:</p> <ul> <li><em><a href="https://cran.r-project.org/web/packages/dcemriS4">dcemriS4</a></em>: R package for the quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) for oncology applications. It provides methods for fitting voxel-wise kinetic models using Levenberg-Marquardt and using a Bayesian framework. Several kinetic models and arterial input functions are available.</li> <li><em><a href="https://cran.r-project.org/web/packages/oro.nifti">oro.nifti</a></em>: Functions for the input/output and visualization of medical imaging data that follow either the ANALYZE, NIfTI or AFNI formats.</li> <li><em><a href="https://bioimaginggroup.github.io/cmr/">cmR</a></em>: cmR: cardiac magnetic resonance imaging in R</li> <li><em><a href="https://github.com/bioimaginggroup/dcemriSpatEnet">dcemri.SpatEnet</a></em>: Analysing DCE-MRI using a spatially regularized Elastic Net.</li> <li><em><a href="https://github.com/bioimaginggroup/dcemriboost">dcemriboost</a></em>: Voxelwise and spatially regularized boosting for DCE-MRI data.</li> <li><em><a href="https://github.com/bioimaginggroup/adaptsmoFMRI">adaptsmoFMRI</a></em>: Adaptive smoothing of fMRI data.</li> </ul>