Fiber tracking uncertainty visualization for neurosurgery
Faizan Siddiqui (TU Delft - Computer Graphics and Visualisation)
A. Vianova – Promotor (TU Delft - Computer Graphics and Visualisation)
E. Eisemann – Promotor (TU Delft - Computer Graphics and Visualisation)
Thomas Höllt – Copromotor (TU Delft - Computer Graphics and Visualisation)
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Abstract
Fiber tracking enables the in-vivo reconstruction of white matter pathways in the brain and has significant potential in clinical workflows such as neurosurgical planning. However, its broader clinical adoption remains limited due to the high degree of uncertainty that arises throughout the processing pipeline, from diffusionMRI acquisition to modeling, tracking, and visualization. These uncertainties are rarely communicated in current clinical visualizations, which often present results as definitive, potentially misleading clinicians and affecting critical decisions.
This thesis explores the integration of fiber tracking uncertainty visualization into neurosurgical workflow, aiming to enhance the interpretability and transparency of the results in a clinical context. A key challenge lies in balancing computational complexity with clear representation, while ensuring the solutions remain aligned with clinical workflows.
To address this, we introduce interactive and computationally efficient visualization approaches that represent uncertainties and support clinicians in understanding how fiber tracking results may vary with inherent uncertainties. These techniques are evaluated through collaborations with medical experts and incorporated into decisionmaking studies to assess their practical relevance.
The contributions presented in this thesis advance the integration of uncertainty into clinical fiber tracking visualization and highlight how embracing uncertainty, rather than ignoring it, can lead to safer and more informed clinical decisions.