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International Journal of Biomedical Imaging
Volume 2014 (2014), Article ID 401819, 14 pages
Research Article

Fiber Visualization with LIC Maps Using Multidirectional Anisotropic Glyph Samples

1Institute for Applied Computer Science (IACS), Stralsund University, Zur Schwedenschanze 15, 18435 Stralsund, Germany
2MR Research Group, Department of Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
3Department of Pediatric Neurology & Developmental Medicine and Experimental Pediatric Neuroimaging, University Children’s Hospital, Hoppe-Seyler-Straße 1, 72076 Tübingen, Germany

Received 25 April 2014; Revised 22 July 2014; Accepted 4 August 2014; Published 28 August 2014

Academic Editor: D. L. Wilson

Copyright © 2014 Mark Höller et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Line integral convolution (LIC) is used as a texture-based technique in computer graphics for flow field visualization. In diffusion tensor imaging (DTI), LIC bridges the gap between local approaches, for example directionally encoded fractional anisotropy mapping and techniques analyzing global relationships between brain regions, such as streamline tracking. In this paper an advancement of a previously published multikernel LIC approach for high angular resolution diffusion imaging visualization is proposed: a novel sampling scheme is developed to generate anisotropic glyph samples that can be used as an input pattern to the LIC algorithm. Multicylindrical glyph samples, derived from fiber orientation distribution (FOD) functions, are used, which provide a method for anisotropic packing along integrated fiber lines controlled by a uniform random algorithm. This allows two- and three-dimensional LIC maps to be generated, depicting fiber structures with excellent contrast, even in regions of crossing and branching fibers. Furthermore, a color-coding model for the fused visualization of slices from T1 datasets together with directionally encoded LIC maps is proposed. The methodology is evaluated by a simulation study with a synthetic dataset, representing crossing and bending fibers. In addition, results from in vivo studies with a healthy volunteer and a brain tumor patient are presented to demonstrate the method's practicality.