Ongeveer 22 uur geleden - Rijksuniversiteit Groningen (RUG) - Groningen
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Insight in subcortical anatomy is vital in brain surgery. Diffusion weighted MRI yields patient specific insight through tractography. Our challenge is to make tractography more reliable and suitable for use in the daily neurosurgical workflow through uncertainty quantification and visualization.
Knowledge of subcortical anatomy is vital in brain surgery. Patient-specific insight can be obtained via diffusion weighted magnetic resonance imaging, the only noninvasive imaging modality for in-vivo assessment of white matter anatomy via so-called tractography.
Tractography refers to reconstruction of putative bundles of axons ('fibre tracts') comprising white matter pathways in the brain. In spite of its potential, tractography is hardly used clinically, for various reasons. It is notoriously hard to infer, apprehend and validate tractography results. As a result, no turn-key clinical protocols are available to assist neurologists and neurosurgeons. On top of this, (intrapatient) uncertainties, related to signal acquisition and reconstruction, model imperfections, sampling, spatial and angular resolution limitations, numerics, parameter estimation, and human factors, accumulate along the visualization pipeline, hampering interpretation. Finally, there is insufficient knowledge of interpatient variability, especially in anatomy disrupting brain diseases, such as tumors. On the positive side, feasibility studies in the context of temporal lobe as well as brain tumor resective surgery have demonstrated that tractography can, in principle, be incorporated into the daily neurosurgical workflow.
Our main challenge in this project is to remove two haunting bottlenecks revealed by these studies: (i) Inherent limitations of diffusion tensor imaging (DTI, the model used in the foregoing study) need to be handled to cope with regions of complex anatomy. (ii) In order to support a semi-automatic clinical tool, tractography needs to be accompanied by reliable estimates and insightful visualizations of its 'limits of accuracy'. If these two goals are met, tractography analysis will become less cumbersome and more reliable, a crucial step towards clinical adoption in the neurosurgical workflow, which is our ultimate goal.
As a PhD student you will be working in a mixed team of mathematicians, computer scientists and clinicians, on a joint project involving the Department of Neurosurgery of Elisabeth Tweesteden Hospital, the Department of Mathematics & Computer Science of Eindhoven University of Technology, and the Faculty of Electrical Engineering, Mathematics and Computer Science of Delft University of Technology. You will be working in the Mathematical Image Analysis group at Eindhoven University of Technology. You will pay occasional visits to the other partners in order to discuss your work with your project collaborators and integrate your results into a comprehensive analysis and visualization pipeline, with the ultimate goal to incorporate this in the daily neurosurgical workflow. In this common endeavour, your specific focus will be on state-of-the-art theory and methodology for diffusion MRI tractrography, including quantification and uncertainty propagation. Findings will be shared with the other partners in the consortium for incorporation into the uncertainty visualization pipeline (partner Delft University of Technology) and for experimentation and validation in a neurosurgical context (partner Elisabeth Tweesteden Hospital).
We are looking for candidates that meet the following requirements: