Enhancing ultrasound shear-wave viscoelastography
Xufei Chen defended her PhD thesis at the department of Electrical Engineering on May 1st.
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Early diagnosis is crucial for both patients and healthcare systems as it allows for the optimization of treatments, such as medical imaging which aid diagnosis via “pictures” of the inside of a patient’s body. One imaging technology is ultrasound imaging, and it’s the instrument to take the very first picture of most of us (while we were still inside our mother’s womb). It’s a non-invasive, non-ionizing, portable, widely available, and highly cost-effective medical imaging approach. For her PhD research, Xufei Chen looked at ways to enhance ultrasound imaging for applications in tissue characterization.
Ultrasound imaging can be used to perform tissue characterization, thus providing additional information on, for instance, tissue mechanical properties that can substantially aid diagnosis.
In fact, diseases can lead to tissue structure and composition alteration, which in turn result in changes in tissue mechanical properties such as stiffness or viscosity.
One ultrasound technique, acoustic-radiation-force-based (ARF-based) shear-wave (SW) viscoelastography, mimics the manual tactual exploration performed by clinicians.
This non-invasive technique is highly controllable in both time and space, and it exploits an ultrasound ARF to locally “push” the tissue and derive tissue viscoelasticity from the measured tissue displacement.
In her PhD thesis, Xufei Chen propose a series of novel signal processing methods to enhance the two main parts of ARF-based SW viscoelastography: (1) SW tracking and (2) viscoelastic tissue characterization using the tracked SWs.
Title of PhD thesis: Enhancing Ultrasound Shear-Wave Viscoelastography by Advanced Signal Processing and Deep Learning. Supervisors: Massimo Mischi, Ruud van Sloun, and Jean-luc Robert.