Illustrative uncertainty visualization of DTI fiber pathways

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 end-user. 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 visualization.R. Brecheisen ( ) · B.M. ter Haar Romeny · A. Vilanova

Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty

“…Amongst focus-based techniques, blur, which is defined as the removal of high-frequency spatial detail from the information [10] has widely been used to indicate fuzziness and ambiguity in the data [7,9,18,31]. For example, Bisantz et al [5] applied blur to a set of airplane symbols to provide decision makers with a fast way to understand the level and uncertainty of a given threat.…”

Multimodality Neurological Data Visualization With Multi-VOI-Based DTI Fiber Dynamic Integration

“…To reduce the complexity of DTI fiber visualization, Brecheisen's group presented a framework that only show DTI fiber tracking uncertainties caused by noise and modeling errors [16]. Golby et al [17] developed a software package that plugged in 3D Slicer [18] to interactively visualize diffusion tensor tractography for improving neurosurgical planning.…”

Diffusion MRI visualization

“…This illustrative rendering shows three different confidence intervals of the optic radiation, with a volume rendering for context. 100 B. Visualization of sensitivity to stopping criteria threshold for corona radiata streamlines. High variability in color indicates sensitivity to threshold.…”