Visually Exploring Differences of DTI Fiber Models
Fiber tracking of Diffusion Tensor Imaging (DTI) datasets is a non-invasive tool to study the underlying fibrous structures in living tissues. However, DTI fibers may vary from subject to subject due to variations in anatomy, motions in scanning, and sign
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State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China [email protected], [email protected], [email protected], [email protected], [email protected] 2 Zhejiang University of Technology, Hangzhou, China [email protected] Computer Science and Engineering, Mississippi State University, Starkville, USA [email protected]
Abstract. Fiber tracking of Diffusion Tensor Imaging (DTI) datasets is a non-invasive tool to study the underlying fibrous structures in living tissues. However, DTI fibers may vary from subject to subject due to variations in anatomy, motions in scanning, and signal noise. In addition, fiber tracking parameters have a great influence on tracking results. Subtle changes of parameters can produce significantly different DTI fibers. Interactive exploration and analysis of differences among DTI fiber models are critical for the purposes of group comparison, atlas construction, and uncertainty analysis. Conventional approaches illustrate differences in the 3D space with either voxel-wise or fiber-based comparisons. Unfortunately, these approaches require an accurate alignment process and might give rise to visual clutter. This paper introduces a two-phase projection technique to reformulate a complex 3D fiber model as a unique 2D map for feature characterization and comparative analysis. To facilitate investigation, regions of significant differences among the 2D maps are further identified. Using these maps, differences that are difficult to be distinguished in the 3D space due to depth occlusion can be easily discovered. We design a visual exploration interface to study differences from multiple perspectives. We evaluate the effectiveness of our approach by examining two datasets.
Keywords: Diffusion tensor imaging alization · Visual exploration
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· Fiber tracking · Difference visu-
Introduction
Diffusion Tensor Imaging (DTI) [2] is a non-invasive in vivo magnetic resonance imaging technique that measures the diffusion of water in biological tissues. In tissues containing fibrous structures, the diffusion is faster along the fibers [14]. By fitting the distribution with a Gaussian model, a DTI tensor volume can c Springer International Publishing Switzerland 2016 A. El Rhalibi et al. (Eds.): Edutainment 2016, LNCS 9654, pp. 333–344, 2016. DOI: 10.1007/978-3-319-40259-8 29
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be reconstructed from the raw Diffusion Weighted Imaging (DWI) volumes [24]. Tracing paths through the entire tensor volume produces a collection of DTI fibers. This process is known as fiber tractography or fiber tracking [1], which has been proven to be a useful technique for analyzing anatomical connectivity. In spite of its potential, DTI remains limited in applications. Uncertainty is a major reason. DTI fibers vary from subject to subject due to variations in anatomy, and from scan to scan because of different subject positions, scanning motions and noises [14]. They are also sensitive to various parameters in tractography such as the integration step size and stopping criteria [3]. To comparatively v
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