Healthcare projects

Below is an overview of the current BSc and MSc student projects. For more detailed information, please click on the respective button. Looking for something specific? Please feel free to contact one of the below researchers directly to discuss the possibilities.

Medical ultrasound techniques

Ultrasound is the most widely used diagnostic technique because of its versatility, ease of use, cost effectiveness, repeatability for patients and reproducibility. Until now the ultrasound diagnosis were based in 2D imaging,
but new developments with integrated electronics has open novel three-dimensional (3D) ultrasound imaging methods and systems for real-time imaging of heart, liver, kidney and other organs but also for monitoring the perfusion of the brains of preterm babies. If you like the combination of signal processing, computer simulations and experimental work and like to apply your work in the medical field than medical ultrasound imaging may just be something for you.

The Maresca Lab: Biomolecular Ultrasound Imaging

We pursue fundamental advances at the interface of ultrasound imaging physics and molecular engineering, taking advantage of the discovery of acoustic biomolecules to interface ultrasound waves with cellular processes. The resulting technologies will be applied in many areas of biology and medicine, and a primary focus of the lab is the non-invasive ultrasound assessment of vascular function at all scales, from arteries down to capillaries and cells.

KDAI Lab: AI for Medical Imaging

Recent advances in AI have led to major breakthroughs in many scientific fields, including medical imaging. Much of the current success can be attributed to the development of powerful deep learning methods that learn from massive data. However, many mysteries of deep learning remain to be unravelled, for example, what is the appropriate inductive bias in specific applications, what leads to vulnerability at test time, and what causes AI uncertainty from a Bayesian point of view. We are a TU Delft AI lab that strives to study such interesting problems, in the domain of medical imaging. We are driven by our curiosity to understand AI and enthusiasm to improve AI for medical imaging applications. Besides, we work on clinical utilization of AI, for various applications such as image quantification, clinical diagnosis, and risk prediction.

Advanced ultrasound imaging devices and non-linear wave propagation

The realization of novel imaging devices and techniques poses many challenges. For example, a matrix array for making 3D medical images contains thousands of piezoelectric elements that can each transmit and receive ultrasound pulses. To deal with this large amount of signals, the elements are directly mounted on an electronic chip that performs the first stage of data processing. Typical challenges are: minimization of crosstalk between the elements, development of beamforming strategies for superfast scanning of a volume, and the development of imaging strategies for achieving superior 3D resolution. Another part of my research involves accurate computation of acoustic waves, in particular non-linear waves that form the basis of medical harmonic imaging. Most projects involve both experimental and numerical work, and can have a component that will be performed at Erasmus MC. 

Medical image analysis

My research focuses on medical image analysis. I aim to develop new techniques to measure relevant features from medical images (e.g. MRI/CT). These analysis techniques are characterized by clever modelling, incorporating prior knowledge and relying strongly on the physical principles of the image formation. If you like to solve a real clinical problem using fundamental theory than you are very welcome to join my group!

Magnetic Resonance Systems Lab (MARS)

At Mars Lab we work all day and night - mostly day - to advance quantitative Magnetic Resonance Imaging (MRI). We pursue two main veins of research. Firstly, we work on the development of novel biomarkers to enable non-invasive assessment of tissue integrity in greater detail and provide insights beyond conventional MRI contrasts. Secondly, we work on accelerated and motion-insensitive quantification of imaging parameters to facilitate clinical translation of quantitative MRI.

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