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http://www.nitrc.org/projects/brainsolution/

A collection of tools for MRI T1 brain image segmentation in the Windows environment. It helps construct a complete pipeline with necessary preprocessing and postprocessing procedures besides brainparser, the core program of our fast brain segmentation. The execution of the whole pipeline can be completed in 2 hours with good segmentation results. Execution requires: FSL

Proper citation: BrainSolution (RRID:SCR_009447) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/frat/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on November 05, 2013. It has been superseeded by the CALATK, available here http://www.calatk.org c++ libraries and applications for performing fluid registration based operations on 2D and 3D images. The registration method is based on the large displacement diffeomorphic mapping (LDDM) registration method and implements discretized fluid registration. This registration method is then applied to time series analysis, cross-sectional atlas building, and longitudinal atlas building. The individual tool components are: * LDDM: Fluid registration between two images. * TimeSeries: Time series analysis of longitudinal data for a single subject. * AtlasBuilder: Cross-sectional atlas building for a population of images. * LongitudinalAtlasBuilder: Longitudinal atlas building for a population of subjects, each with a longitudinal data set. * FRATUtils: A collection of utility functions for working with volumes and time series files

Proper citation: Fluid Registration and Atlas Toolkit (RRID:SCR_009478) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/fips/

A FSL package for the comprehensive management of large-scale multi-site fMRI projects, including data storage, retrieval, calibration, analysis, multi-modal integration, and quality control.

Proper citation: FBIRN Image Processing Scripts (RRID:SCR_009471) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/jalmmse_dwi/

This module reduces Rician noise on nhdr/nrrd DWIs. Filters image in mean squared error sense using Rician noise model. All estimations are performed as sample estimates in a "shaped neighborhood" defined by the weights extracted from structural similarity of voxels following same idea as in Non-Local Means filter.

Proper citation: Joint Anisotropic LMMSE Filter for Stationary Rician noise removal in DWI (RRID:SCR_009502) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dwiregistration/

This code registers linearly and non-linearly Diffusion Weighted Magnetic Resonance Images (DW-MRIs) by extending FLIRT (linear registration of 3D scalar volumes) and FNIRT (non-linear registration of 3D scalar volumes) in the FMRIB Software Library (FSL) to work with 4D volumes. The basis for registering DW-MRIs is the concept of Angular Interpolation (Tao, X., Miller, J. V., 2006. A method forregistering diffusion weighted magnetic resonance images. In: MICCAI. Vol. 9. pp. 594?602), which is implemented and extended to non-linear registration, based on the FLIRT and FNIRT models in FSL. See http://www.frontiersin.org/Brain_Imaging_Methods/10.3389/fnins.2013.00041/abstract. The code does not overwrite FLIRT, FNIRT or any of the FSL C++ code. It is added as FLIRT4D, FNIRT4D and supporting cost functions. The makefiles will however be overwritten to compile the new code, without affecting any version of FSL.

Proper citation: DW-MRI registration in FSL (RRID:SCR_009461) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dbgapcleaner/

Tool to assist site staff with curation of data dictionary, data item, and subject item files for preparation to uploading and sharing data with DbGaP resource.

Proper citation: DbGaP Cleaner (RRID:SCR_009462) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/incf_nidstf/

Program to develop generic standards and tools to facilitate the recording, sharing, and reporting of neuroimaging metadata. It is expected that these efforts will greatly improve upon current practices for archiving and sharing neuroscience data. Neuroscience data, particularly those in neuroinformatics related areas such as neuroimaging and electrophysiology, are associated with a rich set of descriptive information often called metadata. For data archive, storage, sharing and re-use, metadata are of equal importance to primary data, as they define the methods and conditions of data acquisition (such as device characteristics, study/experiment protocol and parameters, behavioral paradigms, and subject/patient information), and statistical procedures. A further challenge for datasharing is the rapidly evolving nature of investigative methods and scientific applications.

Proper citation: INCF Neuroimaging Data Sharing (RRID:SCR_009497) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/idea_lab/

Suite of tools for brain image analysis. Image manipulation, 2D visualization, linear alignment, BBSI, template-based bias correction, skullstrip. GUI Image analysis tools. Now modified to read/write single file nifti (.nii) format. Other packages to be added.

Proper citation: IDeA Lab brain image processing suite (RRID:SCR_009495) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/girt/

A method for group-wise image registration by pairwisely registering similar images identified using graph theoretic techniques. Particularly, they use sparse coding to estimate image similarity measures among images to be registered, yielding asymmetric, group-wise image similarity measures for each image to others in the group.

Proper citation: Groupwise Image Registration Toolbox (RRID:SCR_009492) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/hdni/

An international effort to establish resources necessary to study the application of neuroimaging measures as (surrogate) biomarkers in Huntington''s Disease (HD). The primary aims are to develop and apply software tools, imaging protocols, quality control procedures, data archiving, data distribution, and participation guidelines that will accelerate existing and prospective imaging studies.

Proper citation: HD Neuro-Informatics (RRID:SCR_009493) Copy   

•   Source: SciCrunch Registry

https://github.com/BRAINSia/BRAINSTools/tree/master/BRAINSMush

Tool to generate brain volume mask from input of T1 and T2-weighted images alongside a region of interest brain mask. This volume mask omits dura, skull, eyes, etc. The program is built upon ITK and uses the Slicer3 execution model framework to define the command line arguments and can be fully integrated with Slicer3 using the module discovery capabilities of Slicer3.

Proper citation: BRAINSMush (RRID:SCR_009485) Copy   

•   Source: SciCrunch Registry

http://nrg.wustl.edu/projects/fiv

A tool for visualizing functional and anatomic MRI data.

Proper citation: FIV (RRID:SCR_009575) Copy   

•   Source: SciCrunch Registry

http://www.ncigt.org/pages/Research_Projects/ImagingCoreToolbox/Imaging_Toolkit

This software provides algorithms for the reconstruction of raw MR data. In particular, it supports the reconstruction of accelerated data acquisitions where k-space is subsampled and the Fourier domain encoding is complemented by temporal encoding, spatial encoding, or and/or a constrained reconstruction. This library of functions provides a number of reconstruction algorithms that accurately employ advanced MR imaging methods including: UNFOLD; parallel imaging methods such as SENSE and GRAPPA; Homodyne processing of partial-Fourier data, and gradient field inhomogeneity correction (gradwarp); EPI Nyquist Ghost correction and ramp-sampling gridding. The target audience is research groups who may be interested in exploring or employing advanced MR reconstruction techniques, but don't have the necessary expertise in-house. Inquires may be directed to: ncigt-imaging-toolkit -at- bwh.harvard.edu

Proper citation: NCIGT Fast Imaging Library (RRID:SCR_009609) Copy   

•   Source: SciCrunch Registry

http://www.mazesuite.com/

A complete set of tools that enables researchers to perform spatial and navigational behavior experiments within interactive, easy to create, and extendable (e.g., multiple rooms) 3D virtual environments. MazeSuite can be used to design/edit adapted 3D environments where subjects? behavioral performance can be tracked. Maze Suite consists of three main applications; an editing program to create and alter maps (MazeMaker), a visualization/rendering module (MazeWalker), and finally an analysis/mapping tool (MazeAnalyzer). Additionally, MazeSuite has the capabilities of sending signal pulses to physiological recording devices using standard computer ports. MazeSuite, with all 3 applications, is a unique and complete toolset for researchers who want to easily and rapidly deploy interactive 3D environments. Requirements Maze Suite is designed for Windows 7, Windows Vista and Windows XP. 3D rendering quality depends on available graphics card hardware; OpenGL 2.1 or above compliant is recommended. For Windows XP systems, .NET Framework Version 2.0 or above is required and can be downloaded from Microsoft's website.

Proper citation: MazeSuite (RRID:SCR_009606) Copy   

•   Source: SciCrunch Registry

http://ric.uthscsa.edu/mango/

A viewer for medical research images that provides analysis tools and a user interface to navigate image volumes. There are three versions of Mango, each geared for a different platform: * Mango ? Desktop ? Mac OS X, Windows, and Linux * webMango ? Browser ? Safari, Firefox, Chrome, and Internet Explorer * iMango ? Mobile ? Apple iPad Key Features: * Built-in support for DICOM, NIFTI, Analyze, and NEMA-DES formats * Customizable: Create plugins, custom filters, color tables, file formats, and atlases * ROI Editing: Threshold and component-based tools for painting and tracing ROIs * Surface Rendering: Interactive surface models supporting cut planes and overlays * Image Registration: Semi-automatic image coregistration and manual transform editing * Image Stacking: Threshold and transparency-based image overlay stacking * Analysis: Histogram, cross-section, time-series analysis, image and ROI statistics * Processing: Kernel and rank filtering, arithmetic/logic image and ROI calculators

Proper citation: Mango (RRID:SCR_009603) Copy   

•   Source: SciCrunch Registry

http://visual.cs.utsa.edu/eegvis

A MATLAB toolbox for exploration of multi-channel EEG and other large array-based data sets using multi-scale drill-down techniques. The toolbox can be used directly in MATLAB at any stage in a user's processing pipeline, as a plug in for EEGLAB, or as a standalone precompiled application without MATLAB running. EEGVIS and its supporting packages are freely available under the GNU general public license. The toolbox also supplies a number of extensible base classes for users who wish to develop their own visualizations.

Proper citation: EEGVIS (RRID:SCR_009569) Copy   

•   Source: SciCrunch Registry

https://github.com/clementsan/DTI-Reg

An open-source C++ application that performs pair-wise DTI registration, using scalar FA map to drive the registration. Individual steps of the pair-wise registration pipeline are performed via external applications - some of them being 3D Slicer modules. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit/BRAINSDemonWarp or ANTS -Advanced Normalization Tools-, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via ResampleDTI.

Proper citation: DTI-Reg (RRID:SCR_009560) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/psom/

A lightweight software library to manage complex multi-stage data processing. A pipeline is a collection of jobs, i.e. Matlab or Octave codes with a well identified set of options that are using files for inputs and outputs. To use PSOM, the only requirement is to generate a description of a pipeline in the form of a simple Matlab / Octave structure. PSOM then automatically offers the following services: * Run jobs in parallel using multiple CPUs or within a distributed computing environment. * Generate log files and keep track of the pipeline execution. These logs are detailed enough to fully reproduce the analysis. * Handle job failures : successful completion of jobs is checked and failed jobs can be restarted. * Handle updates of the pipeline : change options or add jobs and let PSOM figure out what to reprocess !

Proper citation: Pipeline System for Octave and Matlab (RRID:SCR_009637) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/picsl_malf/

This package contains a software implementation for joint label fusion and corrective learning, which were applied in MICCAI 2012 Grand Challenge on Multi-Atlas Labeling and finished in the first place. Joint label fusion is for combining candidate segmentations produced by registering and warping multiple atlases for a target image. Corrective learning can be applied to further reduce systematic errors produced by joint label fusion. In general, corrective learning can be applied to correct systematic errors produced by other segmentation methods as well.

Proper citation: PICSL Multi-Atlas Segmentation Tool (RRID:SCR_009633) Copy   

•   Source: SciCrunch Registry

http://bisp.kaist.ac.kr/NIRS-SPM

A SPM and MATLAB-based software package for statistical analysis of near-infrared spectroscopy (NIRS) signals. Based on the general linear model (GLM), and Sun's tube formula / Lipschitz-Killing curvature (LKC) based expected Euler characteristics, NIRS-SPM not only provides activation maps of oxy-, deoxy-, and total-hemoglobin, but also allows for super-resolution activation localization. Additional features, including a wavelet-minimum description length detrending algorithm and cerebral metabolic rate of oxygen (CMRO2) estimation without hypercapnia, were implemented in the NIRS-SPM software package., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NIRS-SPM (RRID:SCR_009630) Copy   

•   Source: SciCrunch Registry