Title:	Anatomy-Focused Volume Line Integral Convolution for Brain White Matter Visualization
Authors:	Schult, Thomas Klose, Uwe Hauser, Till-Karsten Ehricke, Hans
Citation:	WSCG 2019: full papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 73-81.
Issue Date:	2019
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/35611
ISBN:	978-80-86943-37-4 (CD/-ROM)
ISSN:	2464–4617 (print) 2464-4625 (CD/DVD)
Keywords:	integrální konvoluce hlasitosti;lineární konvoluce;anatomický atlas;vizualizace;objemové vykreslování;Difúzní MRI;magnetic resonance imaging
Keywords in different language:	volume line integral convolution;line integral convolution;anatomical atlas;visualization;volume rendering;Diffusion MRI;magnetic resonance imaging
Abstract in different language:	3D visualization of volumetric line integral convolution (LIC) datasets has been a field of constant research. So far, most approaches have focused on finding suitable transfer functions and defining appropriate clipping strategies in order to solve the problem of occlusion. In medicine, extensions of the LIC algorithm to diffusion weighted magnetic resonance imaging (dwMRI) have been proposed, allowing highly resolved LIC volumes to be generated. These are used for brain white matter visualization by LIC slice images, depicting fiber structures with good contrast. However, 3D visualization of fiber pathways by volume rendering faces the problem of occlusion of anatomic regions of interest by the dense brain white matter pattern. In this paper, we introduce an anatomy focused LIC algorithm, which allows specific fiber architectures to be visualized by volume rendering. It uses an anatomical atlas, matched to the dwMRI dataset, during the generation of the LIC noise input pattern. Thus,anatomic fiber structures of interest are emphasized, while surrounding fiber tissue is thinned out and its opacity is modulated. Additionally, we present an adaptation of the orientation-dependent transparency rendering algorithm, which recently has been proposed for fiber streamline visualization, to LIC data. The novel methods are evaluated by application to dwMRI datasets from glioma patients, visualizing fiber structures of interest in the vicinity of the lesion.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2019: Full Papers Proceedings

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