Sharp ultrasound images in ambulances, smartphone-based ultrasound, and better prenatal scans in countries with limited healthcare are coming within reach. Using a new approach to signal processing and deep generative models, TU/e researcher Tristan Stevens is improving both the quality and applicability of medical ultrasound. He defended his PhD research cum laude at the Department of Electrical Engineering on January 26.
Source:Ā Cursor/Nicole Testerink
At the start of his PhD trajectory, Tristan Stevens spent time at the Catharina Hospital, working alongside the Radiology department. It was an eye-opener. While undergoing an ultrasound exam may be barely taxing for a patient, the job involves much more for a radiology technician.
āItās really physical work. Pushing, twisting, often under time pressure. Ultrasound exams come one after another,ā he says. And with an increasingly diverse patient population, the challenges in the ultrasound room are growing as well. One example, Stevens explains, is performing an ultrasound on a patient with a high BMI.
āSound waves are reflected differently by fatty tissue, which leads to more noise in the ultrasound images. That makes it harder to quickly find the right location, and the final images are also much less clear.ā
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