Illustrative uncertainty visualization of DTI fibe

Authors: R Brecheisen B Platel B M ter Haar Romeny A Vilanova

Publish Date: 2012/05/29

Volume: 29, Issue: 4, Pages: 297-309

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Abstract

Diffusion Tensor Imaging DTI and fiber tracking provide unique insight into the 3D structure of fibrous tissues in the brain However the output of fiber tracking contains a significant amount of uncertainty accumulated in the various steps of the processing pipeline Existing DTI visualization methods do not present these uncertainties to the enduser This creates a false impression of precision and accuracy that can have serious consequences in applications that rely heavily on risk assessment and decisionmaking such as neurosurgery On the other hand adding uncertainty to an already complex visualization can easily lead to information overload and visual clutter In this work we propose Illustrative Confidence Intervals to reduce the complexity of the visualization and present only those aspects of uncertainty that are of interest to the user We look specifically at the uncertainty in fiber shape due to noise and modeling errors To demonstrate the flexibility of our framework we compute this uncertainty in two different ways based on 1 fiber distance and 2 the probability of a fiber connection between two brain regions We provide the user with interactive tools to define multiple confidence intervals specify visual styles and explore the uncertainty with a Focus+Context approach Finally we have conducted a user evaluation with three neurosurgeons to evaluate the added value of our visualizationDiffusion is the process of random movement of water molecules over time also called Brownian motion Diffusion Tensor Imaging DTI is an imaging technique based on Magnetic Resonance MR that can measure diffusion of water in living tissues By measuring the amount of diffusion in many different directions the 3D shape of the diffusion profile can be approximated at each point in the tissue In pure water diffusion is unrestricted and has equal magnitude in all directions This results in a spherical or isotropic diffusion profile In fibrous tissues however such as the brain white matter the diffusion will be restricted in directions perpendicular to the fibers This results in a more elongated or anisotropic diffusion profile In DTI the diffusion profile is modeled as a 3D Gaussian probability distribution using a 2ndorder tensor In this model the tensor’s main eigenvector corresponds to the direction of greatest diffusion and is assumed to be aligned with the underlying fiber structure 1 Based on this assumption it is possible to do streamline tracing in the main eigenvector field In the context of DTI this is called fiber tracking 21 27 34 and it allows reconstruction of the fibers in three dimensions Diffusionweighted MRI is the only imaging modality that allows to do this noninvasively and invivo For this reason it has great potential for applications that involve fibrous tissues such as the brain white matter and muscleLeft single fiber obtained from original tensor volume The yellow silhouette represents fixed safety margin Right 100 variations of same fiber based on wild bootstrap method 14 with same safety margin It is clear that it does not cover the possible variations in the tensor dataWith the visualization framework described in this paper we attempt to visually communicate to the neurosurgeon that the fiber tracking algorithm they use may produce a suboptimal reconstruction of the tracts of interest We are specifically focusing on variations in the output due to noise and modeling errors Such information can be captured by different probabilistic algorithms but intuitively showing this information is not a trivial task Even without considering uncertainty diffusion tensor data presents considerable visualization challenges For this reason each tensor is often reduced to a single vector describing the principal direction of diffusion Streamline visualization can then be used to show pathways through the tensor field This gives a good impression of the global structure of fiber tracts However if there are too many streamlines this approach can lead to highly cluttered visualizations such as illustrated in Fig 2 on the right Probabilistic fiber tracking algorithms generally sample thousands of potential pathways in order to compute connection probabilities between different regions of the brain In that case it is no longer feasible nor informative to render each individual streamlineA visualization and interaction framework that uses illustrative silhouettes and outlines to analyze variations in the output of a selected fiber tracking algorithm see Sect 4 A set of interaction widgets that allows easy specification of intervals and their visual styles A Focus+Context uncertainty lens shows confidence intervals only within a userdefined region of interest Outside this region standard fiber visualization can be used allowing for easy comparison between the two visualizations see Sects 43 and 44

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