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.

中文翻译：

---

扩散张量成像中不确定性可视化的渐进方法

扩散张量成像 (DTI) 是一种非侵入性磁共振成像技术，结合纤维跟踪算法，可以表征和可视化大脑中的白质结构。产生的纤维束用于，例如，在肿瘤手术中评估因肿瘤切除而导致的潜在脑功能损伤。从图像采集到最终可视化的 DTI 处理管道相当复杂，在最终结果中会产生不良的不确定性。大多数 DTI 可视化技术不提供有关不确定性存在的任何信息。在计划手术时，通常在纤维束周围使用固定的安全裕度；然而，它无法捕捉不确定性的局部可变性和分布，从而限制了知情决策过程。随机技术是估计 DTI 管道不确定性的一种可能性。然而，它具有很高的计算和内存要求，使其在临床环境中不可行。结果可视化的延迟增加了工作流程的障碍。我们提出了一种渐进式方法，该方法依赖于在渐进式视觉分析范式中使用的野生引导和纤维跟踪的组合。我们提出了一种本地引导策略，它降低了计算和内存成本，并以渐进的方式提供光纤跟踪结果。我们还实现了一种渐进聚合技术，该技术在渐进引导计算过程中计算光纤集合中的距离。