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• W2893595009 abstract "Recently, in diffusion magnetic resonance imaging, the reconstruction and three-dimensional rendering of white matter pathways have been introduced to clinical routine protocols. In a number of clinical situations, for example the preoperative analysis of vascular pathologies, the assessment of spatial relations between vascular structures and nearby fiber pathways is of vital interest for treatment planning. In this paper, we present an approach to the integrated vessel and fiber visualization, based on a novel vascular contrast enhancement operator for Magnetic Resonance Angiography (MRA) datasets. We propose a 3D dynamic programming method, allowing contrast enhancement of vascular structures and suppression of partial voluming effects at vessel borders. This makes it easier to visualize vascular structures by realtime volume rendering with surface shading. In contrast to maximum intensity projection, the method provides better depth cues and allows for easier spatial orientation. The integration of tractography-generated fibers as streamlines or streamtubes with correct visibility computation is performed by a combined volume and geometry renderer. In situations where tractography fails to provide reliable results, we use a line integral convolution method to assess white matter structures. In this manner, the spatial relations of vessels to fiber structures can be depicted by three-dimensional visualizations. We evaluate our approach with clinical data from patients with arteriovenous malformations, stenoses, aneurysms, and from healthy volunteers." @default.
• W2893595009 created "2018-10-05" @default.
• W2893595009 creator A5038676659 @default.
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• W2893595009 date "2018-12-01" @default.
• W2893595009 modified "2023-09-27" @default.
• W2893595009 title "Magnetic resonance angiography contrast enhancement and combined 3D visualization of cerebral vasculature and white matter pathways" @default.
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• W2893595009 doi "https://doi.org/10.1016/j.compmedimag.2018.09.005" @default.
• W2893595009 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30286332" @default.
• W2893595009 hasPublicationYear "2018" @default.
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