ICAI Interview with Ben Luijten: improving ultrasound images with the help of deep learning
Ben Luijten is halfway through his PhD research at e/MTIC AI-Lab and is already working with his team on a number of prototypes of Philips ultrasound devices incorporating their algorithms. Luijten: ‘We work really close to the clinicians right now to find out what the best image quality is for them.’
Ben Luijten is a PhD Candidate at the Biomedical Diagnostic Lab at Eindhoven University of Technology and is a member of ICAI’s e/MTIC AI-Lab.
e/MTIC AI-Lab is a collaboration between Eindhoven University of Technology, Catharina Hospital, Maxima Medical Center, Kempenhaeghe Epilepsy and Sleep Center and Philips.
What can artificial intelligence and deep learning techniques mean for ultrasound imaging?
‘At the core of our research, we are trying to maximize image quality of ultrasound images. Ultrasound imaging has been around for almost fifty years now. It’s a fantastic technique that makes it possible to convert sound reflections into images, and take a look inside the human body in real-time. Technicians however, have always struggled with image quality. MRI and CT scans have a very high image quality, but they don’t give real-time feedback, and are very expensive. We are now trying to improve ultrasound image quality with the use of AI.’
Why is real-time image processing so hard?
‘Our devices have to operate within a fraction of a second. Ultrasound imaging needs at least thirty frames a second, and sometimes even up to a thousand frames a second in the case of blood flow measurements for example. In order to process all this information in real-time, the reconstruction algorithms have to be very small and lightweight in order to run on medical devices.’
What is your approach to this problem?
‘We look at the algorithms early on in the signal chain, so before the image formation. That way we are close to the signal processing. The signal processing is everything that happens in between the measurement and the image formation; the sampling of the signal, putting it into a digital form, filtering it, etc. Since these techniques are already well understood, we try to implement intelligent solutions that stay close to these conventional steps. In doing so, we can improve the image quality in a robust way, with relatively compact neural networks, and a small margin of error.’
How far have you already come with improving the image quality?
‘Well, the first question here is: what actually is the best image quality? Sometimes, what we as technicians find really good images, with very high contrast and resolution for example, the doctors don’t like at all, because they don’t see the same things as they did before. So we work really close to the clinicians right now to find out what the best image quality is for them. At this point we already have some good working AI algorithms that we started to implement in prototype systems.’
How will the next generation of ultrasound devices look like?
‘We want to head into the direction of image processing with small, portable ultrasound devices. That way a doctor could go to a patient’s home to do some basic scanning with lightweight AI running on his or her smartphone. And more complex processing could be done in the cloud. This could be a solution to the triage problem we saw during Covid for example. We did a side project on automatic assessment of Covid-severity, based on ultrasound scans of the lungs. And it worked pretty good. Eventually, the development of these portable ultrasound devices could change the way we use diagnostic tools.’
The e/MTIC AI-Lab promises ‘to provide a fast track to high-tech health innovations’. What does this ‘fast track’ look like?
‘e/MTIC connects a lot of collaborations and people in one lab. e/MTIC Lab tries to create a knowledge hub to minimize the fraction between all the research teams, the industry partners and the hospitals. It really lowers the boundaries of getting in touch with each other. If I have a question on a certain image, they can connect me to a doctor who can help me with that specific image or question.’
What’s next for deep learning in ultrasound imaging?
‘We are going to a certain point where the difference between conventional signal processing and deep learning becomes smaller and smaller. But within medical imaging we’re not likely going towards AI that can do everything. Instead, the focus will be on assisting the doctor in his or her work. In our case, this means developing fast, light-weighted neural networks, that get the most information out of the ultrasound measurements as possible.’
On September 16, 2021, Ben Luijten will talk about ‘Deep learning for ultrasound signal processing’ during the Lunch at ICAI Meetup on Deep Learning in the Netherlands. Want to join? Sign up!