"What I value most are the opportunities you have for both exploring fundamental research and more clinically oriented research.”
"During my Bachelor’s in Biomedical Technology here at UT, I discovered a particular interest in technologies for the imaging of the human body, and the mathematics behind them. It was pretty clear that I wanted to continue in the field of medical imaging, so I went for the specialisation in Imaging & In Vitro Diagnostics within the Master’s in Biomedical Engineering. I chose to stay at the University of Twente because the programme content really spoke to me and also because I already knew the university offers great laboratory facilities and a really good atmosphere.
Broad range of imaging modalities
What made this specialisation interesting for me is the fact that it covers a broad range of imaging modalities, within excellent research groups and facilities. For example, the TechMed Centre has its own MRI scanner that you can use for experiments! It is quite unique to be able to use such an MRI scanner as a student because MRI scanners in hospitals are rarely available for research.
But other than that, there are many other imaging modalities that I learned about. The course Biomedical Optics contains various experiments concerning for example speckle and light scattering. Professors at related research groups are involved in the development of really cool technologies, such as wavefront shaping and photoacoustic mammoscopy. And in a course about Medical Acoustics, we did many experiments concerning for example beam profiles and Doppler imaging, and we also got to image our own organs using a clinical ultrasound machine! But apart from all this practical work, I have also discovered how enthusiastic I could get just by doing mathematics, during the course Mathematical Methods.
Combining two master’s
Apart from image acquisition, I was also eager to learn more about image processing. In the course Advanced Computer Vision & Pattern Recognition, I worked on a project in which we used deep learning for the segmentation of COVID-19 lesions in CT scans of the lungs. I really learned a lot during this project, especially because I was able to collaborate with students of Computer Science.
Since I felt there was much more to learn about image processing than I could fit in the remaining elective space of my Master’s, I decided to combine this Master’s with a second Master’s in Electrical Engineering, specialising in Computer Vision and Biometrics. This allowed me to gain a lot of additional knowledge and skills in for example programming and machine learning. Since a lot of research is done in machine learning-based or -assisted processing of medical images these days, I think these are great assets to have in addition to the skills gained during my Master’s in Biomedical Engineering. Within this double master’s programme, part of my courses counts for both degrees, as do my internship and thesis project.
What I value most about the programme are the opportunities it provides both for exploring fundamental research and more clinically oriented research. This is also reflected in the wide range of choices you have for doing an internship – you are free to choose between going to another university, a research institute or a company, but you can also opt for an internship at a hospital.
Early detection of melanoma
Since I really like doing research, I am considering pursuing a PhD degree after my Master’s. However, before deciding on following that path, I wanted to experience working in the industry. That’s why I chose to do an internship at a company. I worked on a project that aims to develop an intelligent total body scanner for the early detection of melanoma within the healthcare innovation team of Barco, a Belgian tech company that specialises in digital projection and image technology.
During my Master’s, I’ve realised that I’m more interested in image acquisition than in post-processing. My final thesis focuses on the improvement of the acoustic signal strength for Echo Particle Image Velocimetry for blood flow quantification in diseased arteries. These improvements could for example contribute to better predictions about atherosclerotic disease progression and stent patency."