Data Input/Output

DICOM

The industry standard format, for data coming off a clinical imaging device, is DICOM (Digital Imaging and Communications in Medicine). The DICOM "standard" is very broad and very complicated. Roughly speaking each DICOM-compliant file is a collection of fields organized into two four-byte sequences (group,element) that are represented as hexadecimal numbers and form a tag . The (group,element) combination announces what type of information is coming next. There is no fixed number of bytes for a DICOM header. The final (group,element) tag should be the "data" tag (7FE0,0010), such that all subsequent information is related to the image(s).

• The packages oro.dicom, fmri and tractor.base (part of the tractor project) provide R functions that read DICOM files and facilitate their conversion to ANALYZE or NIfTI format.

ANALYZE and NIfTI

Although the industry standard for medical imaging data is DICOM, another format has come to be heavily used in the image analysis community. The ANALYZE format was originally developed in conjunction with an image processing system (of the same name) at the Mayo Foundation. An Anlayze (7.5) format image is comprised of two files, the "hdr" and "img" files, that contain information about the acquisition and the acquisition itself, respectively. A more recent adaption of this format is known as NIfTI-1 and is a product of the Data Format Working Group (DFWG) from the Neuroimaging Informatics Technology Initiative (NIfTI). The NIfTI-1 data format is almost identical to the ANALYZE format, but offers a few improvements: merging of the header and image information into one file (.nii), re-organization of the 348-byte fixed header into more relevant categories and the possibility of extending the header information.

• The packages AnalyzeFMRI, fmri, tractor.base (part of the tractor project), dcemri and NeuroImage (currently only within the neuroim project on R-Forge) all provide functions that read/write ANALYZE and NIfTI files.
• The Rniftilib package provides read/write capabilities for the NIfTI-1 format. The Rniftilib package provides a R-interface to the C reference library provided by the Neuroimaging Informatics Technology Initiative . In contrast to other R-packages supporting the ANALYZE and NIfTI-1 format, this package comes without additional functions for data processing and is restricted to functions for data handling as provided by the C reference library. The aim of the package is to serve as a common basis for the work with multi-dimensional volumetric (neuro)imaging data.

Magnetic Resonance Imaging (MRI)

Diffusion Tensor Imaging (DTI)

• The R-package dti provides structural adaptive smoothing methods for the analysis of diffusion weighted data in the context of the DTI model. Due to its edge preserving properties these smoothing methods are capable of reducing noise without compromizing significant structures (e.g., fibre tracts). The package also provides functions for DTI data processing from input, via tensor reconstruction to visualization (2D and 3D).
• The tractor.base package (part of the tractor project) consists of functions for reading, writing and visualising MRI images. Images may be stored in ANALYZE, NIfTI or DICOM file formats, and can be visualised slice-by-slice or in projection. It also provides functions for common image manipulation tasks, such as masking and thresholding; and for applying arbitrary functions to image data. The package is written in pure R.

Dynamic Contrast-Enhanced MRI (DCE-MRI)

• The dcemri package contains a collection of functions to perform quantitative analysis from a DCE-MRI acquisition on a voxel-by-voxel basis. The steps involved are: motion correction and/or co-registration, T1 estimation, conversion of signal intensity to gadolinium contrast-agent concentration and kinetic parameter estimation. Parametric estimation of the kinetic parameters, from a single-compartment (Kety or extended Kety) model, is performed via Levenburg-Marquardt optimization or Bayesian estimation. Semi-parametric estimation of the kinetic parameters is also possible via Bayesian P-splines.
• The dcemriS4 is an S4 implementation of dcemri with additional capabilities including: read/write for NIfTI extensions, full audit trail, improved visualization, etc.

Functional MRI

• AnalyzeFMRI is a package originally written for the processing and analysis of large structural and functional MRI data sets under the ANALYZE format. It has been updated to include new functionality: complete NIfTI input/output, cross-platform visualization based on Tcl/Tk components, and spatial/temporal ICA ( Independent Components Analysis ) via a graphical user interface (GUI).
• Activated Region Fitting (ARF) is a program for functional magnetic resonance imaging (fMRI) data analysis. The R-Forge project arf uses Gaussian shape spatial models to parameterize active brain regions.
• The R-package fmri provides tools for the analysis of functional MRI data. The core is the implementation of a new class of adaptive smoothing methods. These methods allow for a significant signal enhancement and reduction of false positive detections without, in contrast to traditional non-adaptive smoothing methods, reducing the effective spatial resolution. This property is especially of interest in the analysis of high-resolution functional MRI. The package includes functions for input/output of some standard imaging formats (ANALYZE, NIfTI, AFNI, DICOM) as well as for linear modelling the data and signal detection using Random Field Theory . It also includes ICA and NGCA (non-Gaussian Components Analysis) based methods and hence has some overlap with AnalyzeFMRI.
• Neuroimage is an R package (currently only within the neuroim project on R-Forge) that provides data structures and input/output routines for functional brain imaging data. It reads and writes NIfTI-1 data and provides S4 classes for handling multi-dimensional images.
• Compute Unified Device Architecture (CUDA) is a software platform for massively parallel high-performance computing on NVIDIA GPUs. cudaBayesreg provides a CUDA implementation of a Bayesian multilevel model for the analysis of brain fMRI data. 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.

General Image Processing

• adimpro is a package for 2D digital (color and B/W) images, actually not specific to medical imaging, but for general image processing.
• EBImage is an R package which provides general purpose functionality for the reading, writing, processing and analysis of images. Furthermore, in the context of microscopy-based cellular assays, this package offers tools to transform the images, segment cells and extract quantitative cellular descriptors.

Positron Emission Tomography (PET)

• The PET package contains three of the major iterative reconstruction techniques (Algebraic Reconstruction Technique, Likelihood Reconstruction using Expectation Maximization and Least Squares Conjugate Method) and several direct reconstruction methods for radon transformed data. Furthermore, it offers the possibility to simulate a marked Poisson process with spatial varying intensity.