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http://mipav.cit.nih.gov

A Java-based application that enables quantitative analysis and visualization of medical images of numerous modalities such as DTI, PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders. MIPAV can be run on any Java-enabled platform such as Windows, UNIX, or Macintosh OS X. Functionality includes segmentation, inter- and intra multi-modality registration, surface rendering, volume rendering and reading and writing a large number of biomedical file formats including: DICOM 3.0, Analyze, NIFTI, SPM, MINC, Phillips, GE, Zeiss, Biorad, jpeg, png, tiff, mrc, fits, interfile, and many more.

Proper citation: MIPAV: Medical Image Processing and Visualization (RRID:SCR_007371) Copy   

•   Source: SciCrunch Registry

http://rtimage.sourceforge.net/

An application to visualize, segment, and quantify three-dimensional images. Multiple datasets may be loaded, displayed, fused, processed, and quantitatively analyzed simultaneously. Data may be imported from any DICOM-compatible three dimensional imaging modality. Regions-of-interest may be defined using a number of manual, semi-automatic, and automated tools to segment three-dimensional pixel volumes. They may also be imported from and exported to DICOM structure sets. This software has been applied to preclinical and clinical computed tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), and optical imaging data.

Proper citation: RT Image (RRID:SCR_002535) Copy   

•   Source: SciCrunch Registry

https://github.com/incf-nidash/XCEDE

Data management software that provides an extensive metadata hierarchy for describing and documenting research and clinical studies. The schema organizes information into five general hierarchical levels: a complete project, studies within a project, subjects involved in the studies, visits for each of the subjects, the full description of the subject's participation during each visit.

Proper citation: XCEDE Schema (RRID:SCR_002571) Copy   

•   Source: SciCrunch Registry

http://www.brainvoyager.com/products/brainviewer.html

Software that supports browsing and inspecting essential BrainVoyager data files as well as the header and content of DICOM files. The Viewer supports standard image files (JPEG, GIF, PNG, TIFF, BMP) allowing to inspect snapshots, figures or photos. Users can prepare a folder with selected data of a subject (VMRs, SRFs, Maps, snapshot images), which allows participants of fMRI measurements to browse their brain data and to show it to others. The Viewer can be handed over to colleagues not having a BrainVoyager license together with relevant data. This will allow them to view and explore your analyzed data files.

Proper citation: BrainVoyager Brain Viewer (RRID:SCR_006755) Copy   

•   Source: SciCrunch Registry

https://github.com/cni/nims

A scientific data management system specifically designed for neuroimaging data. NIMS automatically reaps data from the measurement instrument (e.g., MR scanner), sorts and organizes the data based on header information, does some basic processing on the data, and makes the data available to authorized users through a web-based interface. The data are also available from the command-line through a FUSE-based filesystem.

Proper citation: Neurobiological Image Management System (RRID:SCR_006594) Copy   

•   Source: SciCrunch Registry

http://theobjects.com/en/products/scientific/index.php

Software with advanced visualization techniques and state-of-the-art volume rendering provide unparalleled insight into the details and properties of neurological data acquired by CT, micro-CT, MRI, PET, SPECT, microscopy and other modalities. With data fusion tools, intramodality and multimodality registration of MR/CT or PET/CT is easily accomplished, while semi-automatic VOI delineation on fused datasets can improve analysis. Standard formats, such as DICOM, RAW, JPEG, NIFTI, Analyze are supported and 3D/4D sequences can be played. Other features include MPR, oblique, CPR, volume clipping, and surface visualization of cortex, skull, and scalp models. Also standard are easy-to-use tools for voxel-based delineation of features and the measurement of properties, including areas, volumes, counts, and intensity profiles. Present your findings by creating annotated animations or high-resolution images for posters. An SDK is also available to create plug-ins that provide new workflows or functionalities.

Proper citation: ORS Visual SI (RRID:SCR_002509) Copy   

•   Source: SciCrunch Registry

http://www.xnat.org

Software platform designed to facilitate common management and productivity tasks for neuroimaging and associated data.

Proper citation: XNAT - The Extensible Neuroimaging Archive Toolkit (RRID:SCR_003048) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/Software/ProvenanceEditor

A self-contained, platform-independent application that automatically extracts the provenance information from an image header (such as a DICOM image) and generates a data provenance XML file with that information.

Proper citation: LONI Provenance Editor (RRID:SCR_002483) Copy   

•   Source: SciCrunch Registry

http://www.pymvpa.org

A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.

Proper citation: PyMVPA (RRID:SCR_006099) Copy   

•   Source: SciCrunch Registry

http://nifti.nimh.nih.gov/

Coordinated and targeted service, training, and research to speed the development and enhance the utility of informatics tools related to neuroimaging. The initial focus will be on tools that are used in fMRI. If NIfTI proves useful in addressing informatics issues in the fMRI research community, it may be expanded to address similar issues in other areas of neuroimaging. Objectives of NIfTI * Enhancement of existing informatics tools used widely in neuroimaging research * Dissemination of neuroimaging informatics tools and information about them * Community-based approaches to solving common problems, such as lack of interoperability of tools and data * Unique training activities and research career development opportunities to those in the tool-user and tool-developer communities * Research and development of the next generation of neuroimaging informatics tools

Proper citation: Neuroimaging Informatics Technology Initiative (RRID:SCR_003141) Copy   

•   Source: SciCrunch Registry

https://med.virginia.edu/molecular-imaging-core/

Provides imaging small animals and samples with variety of modalities depending on needs including MRI, X-Ray CT, PET, SPECT, luminescence, and fluorescence. Works closely with Radiochemistry Core Lab which can synthesize custom, targeted PET and SPECT imaging agents.Offers preclinical imaging tools designed for imaging rodents.

Proper citation: University of Virginia School of Medicine Molecular Imaging Core Facility (RRID:SCR_023431) Copy   

•   Source: SciCrunch Registry

https://med.virginia.edu/molecular-imaging-core/

Core specializes in imaging small animals and samples with variety of modalities depending on the investigator’s needs. These modalities include MRI, X-Ray CT, PET, SPECT, luminescence, and fluorescence. We work closely with the Radiochemistry Core Lab which can synthesize custom, targeted PET and SPECT imaging agents.

Proper citation: University of Virginia School of Medicine Molecular Imaging Core Facility (RRID:SCR_025472) Copy   

•   Source: SciCrunch Registry