Project: Surface-Based Progressive Uncertainty Visualization in Diffusion Tensor Imaging
Description
Diffusion tensor imaging (DTI) is a magnetic resonance (MR) image technique that is being used to track fibers of white matter in the brain. This fiber tracking, also called tractography, can for example help surgeons with the preparation for brain surgery. During several steps, from the MRI scanner to the visualization of the image, multiple uncertainty sources affects the results. It is important to visualize these uncertainties such that no assumptions have to be made.
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. A progressive approach for uncertainty visualization has been introduced to circumvent this problem. This approach provides a way for a user to interact with the system during expensive computations.
To effectively visualize the results in a progressive pipeline, a robust workflow has to be designed.
The goal of this project is to extend and improve the existing progressive visualization pipeline using fiber hull wrapping. Silhouettes and other Illustrative rendering techniques will be studied and examined for the effective representation of the uncertainty in the fiber tracts. Furthermore, we aim to have a comparison study to analyze and compare the surface-based techniques with other methods for effective uncertainty visualization.
Details
- Supervisor
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Anna Vilanova
- Secondary supervisor
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Faizan Siddiqui
- External location
- This project was never taken