Fiber tracking is a powerful technique that provides valuable insights into the complex white matter structure of the human brain. However, the processing pipeline involves many sources of uncertainty, with one notable factor being the user-defined parameters that significantly influence the resulting outputs. Among these parameters, the definition of seed-points is a crucial aspect in most fiber tracking algorithms. These seed-points are determined through regions of interest (ROI) and serve as the initial points for fiber tract generation. In this work, we present an interactive technique that utilizes seed-point sensitivities to guide the definition of regions of interest (ROI). We examine various scenarios where sensitivity information can enhance the ROI definition process and provide user guidelines and recommended actions for each scenario. Building upon this analysis, we have developed a visualization strategy that enables users to explore seed-point sensitivities effectively and facilitate the definition of optimal ROIs. We present results highlighting the benefits of the proposed visual design in the clinical pipelines.@en

Diffusion-Weighted Magnetic Resonance Imaging (DWI) enables the in-vivo visualization of fibrous tissues such as white matter in the brain. Diffusion-Tensor Imaging (DTI) specifically models the DWI diffusion measurements as a second order-tensor. The processing pipeline to visualize this data, from image acquisition to the final rendering, is rather complex. It involves a considerable amount of measurements, parameters and model assumptions, all of which generate uncertainties in the final result which typically are not shown to the analyst in the visualization. In recent years, there has been a considerable amount of work on the visualization of uncertainty in DWI, and specifically DTI. In this chapter, we primarily focus on DTI given its simplicity and applicability, however, several aspects presented are valid for DWI as a whole. We explore the various sources of uncertainties involved, approaches for modeling those uncertainties, and, finally, we survey different strategies to visually represent them. We also look at several related methods of uncertainty visualization that have been applied outside DTI and discuss how these techniques can be adopted to the DTI domain. We conclude our discussion with an overview of potential research directions.@en

Diffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance imaging technique that, combined with fiber tracking algorithms, allows the characterization and visualization of white matter structures in the brain. The resulting fiber tracts are used, for example, in tumor surgery to evaluate the potential brain functional damage due to tumor resection. The DTI processing pipeline from image acquisition to the final visualization is rather complex generating undesirable uncertainties in the final results. Most DTI visualization techniques do not provide any information regarding the presence of uncertainty. When planning surgery, a fixed safety margin around the fiber tracts is often used; however, it cannot capture local variability and distribution of the uncertainty, thereby limiting the informed decision-making process. Stochastic techniques are a possibility to estimate uncertainty for the DTI pipeline. However, it has high computational and memory requirements that make it infeasible in a clinical setting. The delay in the visualization of the results adds hindrance to the workflow. We propose a progressive approach that relies on a combination of wild-bootstrapping and fiber tracking to be used within the progressive visual analytics paradigm. We present a local bootstrapping strategy, which reduces the computational and memory costs, and provides fiber-tracking results in a progressive manner. We have also implemented a progressive aggregation technique that computes the distances in the fiber ensemble during progressive bootstrap computations. We present experiments with different scenarios to highlight the benefits of using our progressive visual analytic pipeline in a clinical workflow along with a use case and analysis obtained by discussions with our collaborators.

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