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Vacancy number: 2021/0997
Apply to: July 4 2021
Education: University
Employment: Temporary position
Full-time/Part-time: 36 - 36 hours

Seismology of the brain
Cerebral microvessels are crucial for brain health. Disturbances of these vessels, known as small vessel disease (SVD) alter the brain’s blood supply, waste clearance and microstructure (biomechanical tissue properties). SVD causes 25% of all cerebral strokes and is a major cause of cognitive decline, dementia and functional disability. Unfortunately, the microvasculature's small dimensions strongly limit our abilities to non-invasively probe its structure and function, hampering early disease detection, disease-mechanism studies and targeted treatment.

Inspired by seismology, this project aims to develop a new approach in which natural brain pulsations are used as a quantitative probe for microvascular physiology and biomechanical tissue properties. These pulsations will be measured with newly developed, advanced 7T MRI techniques. Next, these 7T MRI measurements will be coupled to advanced computational models that tie observable tissue pulsations to microvascular and biomechanical parameters of interest, including: microvascular blood volume pulsations, tissue stiffness and interstitial pressure fields driving brain waste clearance. By integrating computational models and MRI measurements, this project will deliver tools that can assess these microvascular and biomechanical parameters, which cannot be obtained from either MRI or modeling alone.

Project description
We recently developed ultrahigh-field (7T) MRI methods that measure the subtle heartbeat-induced pulsations in brain tissue motion and deformation. Your role in this project will be to integrate these MRI measurements with models, in order to jointly assess microvascular- and mechanical tissue parameters from the MRI measurements. You will start with implementing and applying a numerical, iterative inversion of the motion model to derive stiffness parameters. This approach will be similar to conventional MR elastographic methods and inversion methods in earth sciences that are used to obtain physical properties of the earth’s interior. Next, you will incorporate more and more anatomical prior knowledge and develop deep-learning based approaches that will be trained on artificial MRI images generated by advanced forward models of the brain pulsations.
Ultimately, the potential of these methods to characterize microvascular properties and mechanical tissue properties, will be studied in patients with SVD.

Department

You will be working at the internationally renowned Center for Image Sciences at the UMC Utrecht, which is housed within the Imaging Division of the UMC Utrecht, enabling close collaboration between image scientists and radiologists. You will work in a team of PhD students and postdocs in the field of ultra-high-field strength (7 Tesla) Magnetic Resonance Imaging (MRI). There will also be a close collaboration with experts in MRI Elastography reconstructions from the department of Mechanical Engineering of the University of Sherbrooke, Canada.

Profile

You are an excellent candidate with an MSc in (bio- or geo)physics, mathematics, image sciences or a similar field, and with a strong interest in modeling and parameter estimation. You have a good scientific background, are highly motivated and independent, and able to work in an interdisciplinary team of engineers and medical doctors. Programing experience is required, and knowledge of C++ is an advantage.

The maximum salary for this position (36 - 36 hours) is € 3.196,00 gross per month based on full-time employment.

In addition, we offer an annual benefit of 8.3%, holiday allowance, travel expenses and career opportunities. The terms of employment are in accordance with the Cao University Medical Centers (UMC).

More information

If you have any questions about this vacancy, please contact Jaco Zwanenburg, Associate professor, phone number: +31887551394, e‑mail adress: J.J.M.Zwanenburg@umcutrecht.nl.

Acquisition based on this jobopening is not appreciated.

*LI-UMC

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