Fiber Visualization with LIC Maps Using Multidirectional Anisotropic Glyph Samples.

Mark Höller,Uwe Klose,Kay-M Otto,Hans-H Ehricke,Samuel Groeschel

doi:10.1155/2014/401819

Abstract

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.

Highlights

In diffusion-weighted magnetic resonance imaging (DWMRI), only a few white matter visualization approaches have gained clinical relevance despite the introduction of several novel imaging techniques, such as high angular resolution diffusion imaging (HARDI)
In a previous paper we described an adaptation of the Line integral convolution (LIC) method to high angular resolution diffusion imaging (HARDI), utilizing orientation distribution functions (ODFs) as representations of the local diffusion profile
Instead of elliptical or ODF-based samples, we propose the use of three-dimensional glyph samples derived from the fiber orientation distribution (FOD) as the input pattern for the LIC algorithm

Summary

Introduction

In diffusion-weighted magnetic resonance imaging (DWMRI), only a few white matter visualization approaches have gained clinical relevance despite the introduction of several novel imaging techniques, such as high angular resolution diffusion imaging (HARDI). More complex approaches using hyperstreamlines [6] and tensor lines [7] have been explored Whilst these methods reveal global relationships, such as connections between brain regions, they fail to reliably depict uncertainties in the presented fiber anatomy, due to problems of data acquisition and signal processing. Rather, they represent data interpretations and depend on processing parameters, including choice of seed regions, tracking algorithms, and track termination criteria. This is true for results of feature extraction methods [8], which generate a pathway’s complex hull, for example, by segmentation or fiber clustering [9,10,11,12]

Methods

Results

Conclusion