Browsing by Author "Siddiqui, Faizan"

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    Interactive Visual Exploration of Region-based Sensitivities in Fiber Tracking
    (The Eurographics Association, 2023) Siddiqui, Faizan; Höllt, Thomas; Vilanova, Anna; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    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.
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    Parameter Sensitivity and Uncertainty Visualization in DTI
    (The Eurographics Association, 2022) Siddiqui, Faizan; Höllt, Thomas; Vilanova, Anna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Diffusion Tensor Imaging is a powerful technique that provides a unique insight into the complex structure of the brain's white matter. However, several sources of uncertainty limit its widespread use. Data and modeling errors arise due to acquisition noise and modeling transformations. Moreover, the sensitivities of the user-defined parameters and region definitions are not usually evaluated, a small change in these parameters can add large variations in the results. Without showing these uncertainties any visualization of DTI data can potentially be misleading. In our work, we develop a visual analytic tool that provides insight into the accumulated uncertainty in the visualization pipeline. The primary goal of this project is to develop an efficient visualization strategy that will assist the end-user in making critical decisions and make fiber tracking analysis less cumbersome and more reliable, a crucial step towards adoption in the neurosurgical workflow.