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PhD position in diffusion MRI tractography
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.
- de Rondom, Eindhoven, Noord-Brabant
- Tijdelijk contract / Tijdelijke opdracht
- Uren per week:
- 38 uur
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:
- a challenging job in a dynamic and ambitious university and a stimulating research environment;
- a full time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months;
- a gross salary of €2.222 per month in the first year increasing up to €2.840 in the fourth year;
- healthy travel funding for presenting your work at the leading conferences and for research visits;
- support for your personal development and career planning including courses, summer schools, conference visits etc.;
- an extensive package of fringe benefits (e.g. support in moving expenses and commuting expenses, excellent technical infrastructure, on-campus child care, and excellent sports facilities, extra holiday allowance (8%, May), and end-of-year bonus (8.3%, December)).
For more information about the position and the project, please contact prof.dr. Luc Florack by e-mail, email@example.com
For more information about the employment conditions, please contact drs. Marjolein von Reth (HR advisor) by telephone +31 40 247 5722 or by e-mail, firstname.lastname@example.org
The application should consist of the following parts:
- Cover letter explaining your motivation and qualifications for the position;
- Detailed Curriculum Vitae;
- Key publications (or links to download).
- A copy or a link to your Master thesis. If you have not completed it yet, please explain your current situation.
- A transcript of your grades.
Selected candidates may be invited first for a Skype interview and then for onsite visits to TU/e.
The selection process will start immediately and will continue until the position gets filled. The position is fully funded and immediately available.
You can apply by using the 'Apply for this job' button. Please note that a maximum of 5 documents of each 2 MB each can be uploaded. If you have more than 5 documents you will have to combine them. Incomplete applications or applications vi e-mail will not be considered.
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