Department of Electronic and Computer Engineering Seminar - Deep Learning at the Ultrasound Imaging Front-End
 

Ultrasound is undoubtedly a popular medical imaging modality and is becoming known for its high-frame-rate imaging capabilities. However, high-frame-rate ultrasound has yet to flourish in point-of-care applications due to the lack of suitable portable hardware, and its ability to offer time-resolved flow visualization is hampered by Doppler aliasing artifacts. Can we take advantage of deep learning to overcome bottlenecks in high-frame-rate system design? Can we design neural networks to resolve Doppler aliasing artifacts in real time? This seminar will introduce our laboratory’s quest to learn deep and learn smart about ultrasound imaging systems to make high-frame-rate ultrasound viable for portable use and flow estimation. We will demonstrate how deep learning solutions can be devised to resolve data transfer bottlenecks in ultrasound systems and, in turn, enable robust generation of high-frame-rate ultrasound images with data acquired from few array channels. We will also show how deep learning has enabled the design of advanced Doppler flow imaging platforms with lucid flow visualization performance. Related algorithms, real-time engineering efforts, and clinical applications will be presented throughout the presentation.