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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

Data handling complexity in MRI analysis streams. A schematic representation of the some of the levels of abstraction considered when preparing software capable of handling multi-modal neuroimaging data. (A) The technology type used: Here we use MRI as an example. (B) Some MRI acquisition protocols or sub-types: a researcher using a combination of protocols may, for example, be looking for changes in blood oxygenation using functional MRI, localizing the regions of activation to specific brain regions using structural MRI, and then looking for anatomical connections between these regions using Diffusion weighted MRI. They may then wish to overlay the results from each modality to explore spatial relationships. (C) Examples of the types of different analysis algorithms and routines for any given protocol. (D) Examples of data formats: although researchers may use the same technology, the same protocol, and even the same analysis technique/algorithm, they may save their results in different file formats not immediately accessible to software utilized at other sites. *In the case of Fiber tract files, few standard file formats have been developed specifically for DTI data, and even fewer for saving the results of fiber tracking algorithm output. The.nrrd file format (http://www.na-mic.org/Wiki/index.php/NAMIC_Wiki:DTI:Nrrd_format) is used by 3D Slicer to load DTI values and parameters into memory. Fibers are subsequently calculated and can be saved to a vtk file format, unspecific for DTI fibers but useful for import and conversion by any VTK based programs, including DV3D.
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Figure 2: Data handling complexity in MRI analysis streams. A schematic representation of the some of the levels of abstraction considered when preparing software capable of handling multi-modal neuroimaging data. (A) The technology type used: Here we use MRI as an example. (B) Some MRI acquisition protocols or sub-types: a researcher using a combination of protocols may, for example, be looking for changes in blood oxygenation using functional MRI, localizing the regions of activation to specific brain regions using structural MRI, and then looking for anatomical connections between these regions using Diffusion weighted MRI. They may then wish to overlay the results from each modality to explore spatial relationships. (C) Examples of the types of different analysis algorithms and routines for any given protocol. (D) Examples of data formats: although researchers may use the same technology, the same protocol, and even the same analysis technique/algorithm, they may save their results in different file formats not immediately accessible to software utilized at other sites. *In the case of Fiber tract files, few standard file formats have been developed specifically for DTI data, and even fewer for saving the results of fiber tracking algorithm output. The.nrrd file format (http://www.na-mic.org/Wiki/index.php/NAMIC_Wiki:DTI:Nrrd_format) is used by 3D Slicer to load DTI values and parameters into memory. Fibers are subsequently calculated and can be saved to a vtk file format, unspecific for DTI fibers but useful for import and conversion by any VTK based programs, including DV3D.

Mentions: When considering the data types that a multi-modal neuroimaging visualization tool may be required to handle, there are at least four levels of abstraction we need to consider. An example of the complexity of the data structures that require consideration for neuroimaging data processing streams is shown in Figure 2. Analyzing and presenting data from MRI protocol subtypes alone requires a support for a broad range of data formats. A software package capable of supporting multi-modal data thus needs to consider: (a) the technology being used to acquire the different data types, (b) the acquisition settings (or protocol) being used to acquire the data, (c) the analysis techniques used to analyze the acquired data, and (d) the format in which the data and results are stored.


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)

Data handling complexity in MRI analysis streams. A schematic representation of the some of the levels of abstraction considered when preparing software capable of handling multi-modal neuroimaging data. (A) The technology type used: Here we use MRI as an example. (B) Some MRI acquisition protocols or sub-types: a researcher using a combination of protocols may, for example, be looking for changes in blood oxygenation using functional MRI, localizing the regions of activation to specific brain regions using structural MRI, and then looking for anatomical connections between these regions using Diffusion weighted MRI. They may then wish to overlay the results from each modality to explore spatial relationships. (C) Examples of the types of different analysis algorithms and routines for any given protocol. (D) Examples of data formats: although researchers may use the same technology, the same protocol, and even the same analysis technique/algorithm, they may save their results in different file formats not immediately accessible to software utilized at other sites. *In the case of Fiber tract files, few standard file formats have been developed specifically for DTI data, and even fewer for saving the results of fiber tracking algorithm output. The.nrrd file format (http://www.na-mic.org/Wiki/index.php/NAMIC_Wiki:DTI:Nrrd_format) is used by 3D Slicer to load DTI values and parameters into memory. Fibers are subsequently calculated and can be saved to a vtk file format, unspecific for DTI fibers but useful for import and conversion by any VTK based programs, including DV3D.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2666199&req=5

Figure 2: Data handling complexity in MRI analysis streams. A schematic representation of the some of the levels of abstraction considered when preparing software capable of handling multi-modal neuroimaging data. (A) The technology type used: Here we use MRI as an example. (B) Some MRI acquisition protocols or sub-types: a researcher using a combination of protocols may, for example, be looking for changes in blood oxygenation using functional MRI, localizing the regions of activation to specific brain regions using structural MRI, and then looking for anatomical connections between these regions using Diffusion weighted MRI. They may then wish to overlay the results from each modality to explore spatial relationships. (C) Examples of the types of different analysis algorithms and routines for any given protocol. (D) Examples of data formats: although researchers may use the same technology, the same protocol, and even the same analysis technique/algorithm, they may save their results in different file formats not immediately accessible to software utilized at other sites. *In the case of Fiber tract files, few standard file formats have been developed specifically for DTI data, and even fewer for saving the results of fiber tracking algorithm output. The.nrrd file format (http://www.na-mic.org/Wiki/index.php/NAMIC_Wiki:DTI:Nrrd_format) is used by 3D Slicer to load DTI values and parameters into memory. Fibers are subsequently calculated and can be saved to a vtk file format, unspecific for DTI fibers but useful for import and conversion by any VTK based programs, including DV3D.
Mentions: When considering the data types that a multi-modal neuroimaging visualization tool may be required to handle, there are at least four levels of abstraction we need to consider. An example of the complexity of the data structures that require consideration for neuroimaging data processing streams is shown in Figure 2. Analyzing and presenting data from MRI protocol subtypes alone requires a support for a broad range of data formats. A software package capable of supporting multi-modal data thus needs to consider: (a) the technology being used to acquire the different data types, (b) the acquisition settings (or protocol) being used to acquire the data, (c) the analysis techniques used to analyze the acquired data, and (d) the format in which the data and results are stored.

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