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This package provides an R interface to the NiftyReg image registration tools <>.


Various methods for MRI tissue classification.


The package implements a Dirichlet Process Mixture (DPM) model for clustering and image segmentation. The DPM model is a Bayesian nonparametric methodology that relies on MCMC simulations for exploring mixture models with an unknown number of components. The code implements conjugate models with normal structure (conjugate normal-normal DP mixture model). The package's applications are oriented towards the classification of magnetic resonance images according to tissue type or region of interest.


Functions for the input/output and visualization of medical imaging data that follow either the ANALYZE, NIfTI or AFNI formats. This package is part of the Rigorous Analytics bundle.


Data input/output functions for data that conform to the Digital Imaging and Communications in Medicine (DICOM) standard, part of the Rigorous Analytics bundle.


A collection of routines and documentation that allows one to perform voxel-wise quantitative analysis of dynamic contrast-enhanced or diffusion-weighted MRI data, with emphasis on oncology applications.


Compute Unified Device Architecture (CUDA) is a software platform for massively parallel high-performance computing on NVIDIA GPUs. This package provides a CUDA implementation of a Bayesian multilevel model for the analysis of brain fMRI data. A fMRI data set consists of time series of volume data in 4D space. Typically, volumes are collected as slices of 64 x 64 voxels. Analysis of fMRI data often relies on fitting linear regression models at each voxel of the brain. The volume of the data to be processed, and the type of statistical analysis to perform in fMRI analysis, call for high-performance computing strategies. In this package, the CUDA programming model uses a separate thread for fitting a linear regression model at each voxel in parallel. The global statistical model implements a Gibbs Sampler for hierarchical linear models with a normal prior. This model has been proposed by Rossi, Allenby and McCulloch in `Bayesian Statistics and Marketing', Chapter 3, and is referred to as `rhierLinearModel' in the R-package bayesm. A notebook equipped with a NVIDIA `GeForce 8400M GS' card having Compute Capability 1.1 has been used in the tests. The data sets used in the package's examples are available in the separate package cudaBayesregData.


R interface to nifticlib (nifticlib-2.0.0) (read/write ANALYZE(TM)7.5/NIfTI-1 volume images)


The tractor.base package consists of functions for working with magnetic resonance images. It can read and write image files stored in Analyze, NIfTI, MGH and DICOM formats (DICOM support is read only), generate images for use as regions of interest, and manipulate and visualise images.


Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), structural adaptive smoothing in in case of (DTI) (K. Tabelow, J. Polzehl, V. Spokoiny, and H.U. Voss, Diffusion Tensor Imaging: Structural Adaptive Smoothing, Neuroimage 39(4), 1763-1773 (2008)), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.