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

Gouws A, Woods W, Millman R, Morland A, Green G - Front Neuroinform (2009)

Bottom Line: Formats for other data types are supported.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, York NeuroImaging Centre University of York UK.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

3D overlay data using isosurface transparency. (A) 2D overlay data from an fMRI experiment overlaid onto a structural MRI volume. (B) The vtkContourFilter can be applied to create an isosurface through the data at a specific threshold value, say z = 2.3. The returned 3D surfaces will encompass all areas in the data set that have a z-score of z = 2.3 or above. We could repeat the process, asking the vtkContourFilter to return smaller surfaces as we increase the threshold. (C) A 2D representation (using isocontours shown in blue) of 2 separate isovalues used to extract surfaces. (D) If we simultaneously render five sets of surfaces, at z-scores of z = 2.3, 3.3, 4.3, 5.3, and 6.3, for example, the only set of surfaces visible would be that at z = 2.3, since all other surfaces are inside this surface. We can manipulate the transparency and color of the vtkPolyData class to make the distribution of activation visible and overcome this problem. By making the outermost surface (at the lowest threshold value) 80% transparent, the second outermost 60% transparent, the third 40% transparent, the fourth 20% transparent, and the highest threshold surface completely opaque, we make all surfaces simultaneously visible. To emphasize this effect, we can also apply a color gradient (yellow to red) across the surface threshold range. Interacting with this mode of visualization in 3D gives an instantaneous percept of the entire distribution of the activation in 3D. (E) This image shows a number of tracts output from FSL's Probtrack toolbox rendered using the 3D overlay technique. The tracts are seen as yellow to red isosurfaces. The green spheres indicate the positions of seed and target points as defined in Probtrack.
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Figure 8: 3D overlay data using isosurface transparency. (A) 2D overlay data from an fMRI experiment overlaid onto a structural MRI volume. (B) The vtkContourFilter can be applied to create an isosurface through the data at a specific threshold value, say z = 2.3. The returned 3D surfaces will encompass all areas in the data set that have a z-score of z = 2.3 or above. We could repeat the process, asking the vtkContourFilter to return smaller surfaces as we increase the threshold. (C) A 2D representation (using isocontours shown in blue) of 2 separate isovalues used to extract surfaces. (D) If we simultaneously render five sets of surfaces, at z-scores of z = 2.3, 3.3, 4.3, 5.3, and 6.3, for example, the only set of surfaces visible would be that at z = 2.3, since all other surfaces are inside this surface. We can manipulate the transparency and color of the vtkPolyData class to make the distribution of activation visible and overcome this problem. By making the outermost surface (at the lowest threshold value) 80% transparent, the second outermost 60% transparent, the third 40% transparent, the fourth 20% transparent, and the highest threshold surface completely opaque, we make all surfaces simultaneously visible. To emphasize this effect, we can also apply a color gradient (yellow to red) across the surface threshold range. Interacting with this mode of visualization in 3D gives an instantaneous percept of the entire distribution of the activation in 3D. (E) This image shows a number of tracts output from FSL's Probtrack toolbox rendered using the 3D overlay technique. The tracts are seen as yellow to red isosurfaces. The green spheres indicate the positions of seed and target points as defined in Probtrack.

Mentions: This visualization technique relies on the previously described method for extracting isosurfaces from MRI volumes using the vtkContourFilter. We previously described extracting a rough representation of the cortex by passing a base sMRI volume to the vtkContourFilter. Following the same principle, we can pass an overlay volume to the vtkContourFilter in the place of the structural volume. This volume could, for example, be a statistical z-score map of the activation resulting from a contrast analysis of fMRI data. This is illustrated with a visual motion fMRI data set in Figure 8. The 2D overlay data is shown in Figure 8A.


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

Gouws A, Woods W, Millman R, Morland A, Green G - Front Neuroinform (2009)

3D overlay data using isosurface transparency. (A) 2D overlay data from an fMRI experiment overlaid onto a structural MRI volume. (B) The vtkContourFilter can be applied to create an isosurface through the data at a specific threshold value, say z = 2.3. The returned 3D surfaces will encompass all areas in the data set that have a z-score of z = 2.3 or above. We could repeat the process, asking the vtkContourFilter to return smaller surfaces as we increase the threshold. (C) A 2D representation (using isocontours shown in blue) of 2 separate isovalues used to extract surfaces. (D) If we simultaneously render five sets of surfaces, at z-scores of z = 2.3, 3.3, 4.3, 5.3, and 6.3, for example, the only set of surfaces visible would be that at z = 2.3, since all other surfaces are inside this surface. We can manipulate the transparency and color of the vtkPolyData class to make the distribution of activation visible and overcome this problem. By making the outermost surface (at the lowest threshold value) 80% transparent, the second outermost 60% transparent, the third 40% transparent, the fourth 20% transparent, and the highest threshold surface completely opaque, we make all surfaces simultaneously visible. To emphasize this effect, we can also apply a color gradient (yellow to red) across the surface threshold range. Interacting with this mode of visualization in 3D gives an instantaneous percept of the entire distribution of the activation in 3D. (E) This image shows a number of tracts output from FSL's Probtrack toolbox rendered using the 3D overlay technique. The tracts are seen as yellow to red isosurfaces. The green spheres indicate the positions of seed and target points as defined in Probtrack.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2666199&req=5

Figure 8: 3D overlay data using isosurface transparency. (A) 2D overlay data from an fMRI experiment overlaid onto a structural MRI volume. (B) The vtkContourFilter can be applied to create an isosurface through the data at a specific threshold value, say z = 2.3. The returned 3D surfaces will encompass all areas in the data set that have a z-score of z = 2.3 or above. We could repeat the process, asking the vtkContourFilter to return smaller surfaces as we increase the threshold. (C) A 2D representation (using isocontours shown in blue) of 2 separate isovalues used to extract surfaces. (D) If we simultaneously render five sets of surfaces, at z-scores of z = 2.3, 3.3, 4.3, 5.3, and 6.3, for example, the only set of surfaces visible would be that at z = 2.3, since all other surfaces are inside this surface. We can manipulate the transparency and color of the vtkPolyData class to make the distribution of activation visible and overcome this problem. By making the outermost surface (at the lowest threshold value) 80% transparent, the second outermost 60% transparent, the third 40% transparent, the fourth 20% transparent, and the highest threshold surface completely opaque, we make all surfaces simultaneously visible. To emphasize this effect, we can also apply a color gradient (yellow to red) across the surface threshold range. Interacting with this mode of visualization in 3D gives an instantaneous percept of the entire distribution of the activation in 3D. (E) This image shows a number of tracts output from FSL's Probtrack toolbox rendered using the 3D overlay technique. The tracts are seen as yellow to red isosurfaces. The green spheres indicate the positions of seed and target points as defined in Probtrack.
Mentions: This visualization technique relies on the previously described method for extracting isosurfaces from MRI volumes using the vtkContourFilter. We previously described extracting a rough representation of the cortex by passing a base sMRI volume to the vtkContourFilter. Following the same principle, we can pass an overlay volume to the vtkContourFilter in the place of the structural volume. This volume could, for example, be a statistical z-score map of the activation resulting from a contrast analysis of fMRI data. This is illustrated with a visual motion fMRI data set in Figure 8. The 2D overlay data is shown in Figure 8A.

Bottom Line: Formats for other data types are supported.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, York NeuroImaging Centre University of York UK.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus