DataViewer3D: An Open-Source, Cross-Platform Multi-Modal Neuroimaging Data Visualization Tool

Integration and display of results from multiple neuroimaging modalities [e.g. magnetic resonance imaging (MRI), magnetoencephalography, EEG] relies on display of a diverse range of data within a common, defined coordinate frame. DataViewer3D (DV3D) is a multi-modal imaging data visualization tool offering a cross-platform, open-source solution to simultaneous data overlay visualization requirements of imaging studies. While DV3D is primarily a visualization tool, the package allows an analysis approach where results from one imaging modality can guide comparative analysis of another modality in a single coordinate space. DV3D is built on Python, a dynamic object-oriented programming language with support for integration of modular toolkits, and development of cross-platform software for neuroimaging. DV3D harnesses the power of the Visualization Toolkit (VTK) for two-dimensional (2D) and 3D rendering, calling VTK's low level C++ functions from Python. Users interact with data via an intuitive interface that uses Python to bind wxWidgets, which in turn calls the user's operating system dialogs and graphical user interface tools. DV3D currently supports NIfTI-1, ANALYZE™ and DICOM formats for MRI data display (including statistical data overlay). Formats for other data types are supported. The modularity of DV3D and ease of use of Python allows rapid integration of additional format support and user development. DV3D has been tested on Mac OSX, RedHat Linux and Microsoft Windows XP. DV3D is offered for free download with an extensive set of tutorial resources and example data.

[1]  Carrie R. McDonald,et al.  The use of neuroimaging to study behavior in patients with epilepsy , 2008, Epilepsy & Behavior.

[2]  B. Rosen,et al.  Mapping cognitive function. , 2007, Neuroimaging clinics of North America.

[3]  Guillermo Sapiro,et al.  Creating connected representations of cortical gray matter for functional MRI visualization , 1997, IEEE Transactions on Medical Imaging.

[4]  J. Talairach,et al.  Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging , 1988 .

[5]  AD Gouws,et al.  DataViewer3D: An open-source, cross-platform multi-modal neuroimaging data visualization tool , 2009, NeuroImage.

[6]  Michael J. Martinez,et al.  Bias between MNI and Talairach coordinates analyzed using the ICBM‐152 brain template , 2007, Human brain mapping.

[7]  若菜 勢津.  Fiber tract-based atlas of human white matter anatomy , 2006 .

[8]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[9]  Jonas Larsson,et al.  Imaging vision : Functional mapping of intermediate visual processes in man , 2001 .

[10]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[11]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[12]  Bin He,et al.  Effects of fMRI–EEG mismatches in cortical current density estimation integrating fMRI and EEG: A simulation study , 2006, Clinical Neurophysiology.

[13]  B. Wandell,et al.  Visualization and Measurement of the Cortical Surface , 2000, Journal of Cognitive Neuroscience.

[14]  M. Coltheart.  What has Functional Neuroimaging told us about the Mind (so far)? (Position Paper Presented to the European Cognitive Neuropsychology Workshop, Bressanone, 2005) , 2006, Cortex.

[15]  Arthur W. Toga,et al.  Provenance in neuroimaging , 2008, NeuroImage.