Interactive Volume Rendering of Diffusion Tensor Data [electronic resource]
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| Online Access: |
Online Access |
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| Corporate Author: | |
| Format: | Government Document Electronic eBook |
| Language: | English |
| Published: |
Berkeley, Calif. : Oak Ridge, Tenn. :
Lawrence Berkeley National Laboratory ; distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy,
2007.
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| Abstract: | As 3D volumetric images of the human body become an increasingly crucial source of information for the diagnosis and treatment of a broad variety of medical conditions, advanced techniques that allow clinicians to efficiently and clearly visualize volumetric images become increasingly important. Interaction has proven to be a key concept in analysis of medical images because static images of 3D data are prone to artifacts and misunderstanding of depth. Furthermore, fading out clinically irrelevant aspects of the image while preserving contextual anatomical landmarks helps medical doctors to focus on important parts of the images without becoming disoriented. Our goal was to develop a tool that unifies interactive manipulation and context preserving visualization of medical images with a special focus on diffusion tensor imaging (DTI) data. At each image voxel, DTI provides a 3 x 3 tensor whose entries represent the 3D statistical properties of water diffusion locally. Water motion that is preferential to specific spatial directions suggests structural organization of the underlying biological tissue; in particular, in the human brain, the naturally occuring diffusion of water in the axon portion of neurons is predominantly anisotropic along the longitudinal direction of the elongated, fiber-like axons [MMM+02]. This property has made DTI an emerging source of information about the structural integrity of axons and axonal connectivity between brain regions, both of which are thought to be disrupted in a broad range of medical disorders including multiple sclerosis, cerebrovascular disease, and autism [Mos02, FCI+01, JLH+99, BGKM+04, BJB+03] |
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| Item Description: | Published through the Information Bridge: DOE Scientific and Technical Information. 03/30/2007. "lbnl-2286e" Weber, Gunther; Hamann, Bernd; Hlawitschka, Mario; Anwander, Alfred; Carmichael, Owen; Scheuermann, Gerik. Computational Research Division. |