Prof Zion Tse


Research Overview

Digital Health, Smart Wearables, Mobile Health, Human-Computer Interaction, AI Medical Imaging, Computer-aided Diagnosis, Digital Mental Health, Medical Robotics

My research is at the intersection of Digital Health and Wellbeing, AI-assisted Imaging, Computer-aided Diagnosis, Smart Wearables and Robotics. My long-term aims are to develop translation technologies for precise diagnosis and personalised treatments.

  • Direction 1: Computer-Aided Cancer Treatment with MRI-Guided Focal Laser Prostate Ablation
  • Direction 2: Wireless Seismocardiogram Sensor for Cardiac Arrhythmia Management 
  • Direction 3: AI & Digital Platform for Healthcare and Physiological Monitoring

I am actively seeking collaboration with academic and industry partners as well as incoming PhD students and research fellows through schemes such as the Marie SkÅ‚odowska-Curie Fellowships  and Chinese Scholarship Council (CSC) Awards. 

Prostate Diagnosis and Treatment

Image-guided prostate therapy is a minimally invasive approach for cancer diagnosis and treatment that better preserves the neurovascular bundles. Focal treatment such as laser ablation delivers well-controlled thermal energy at the target tissue and efficiently covers suspected tumour areas.

MRI-Guided Robotics


Robotic needle guidance devices could deliver medical instruments to the diseased tissue percutaneously for biopsies, ablation and drug delivery under image guidance such as Magnetic Resonance Imaging, enhancing existing invasive and time-consuming techniques for tumour targeting. 


Imaging, Navigation and Steering

Multimodality image-guided operating techniques, including advanced imaging, tracking, navigation and steering of medical instruments allows precision image-guided surgeries performed with robotic tools under real-time guidance.

Patient Monitoring

We develop techniques to measure high-fidelity electrocardiograms for MRI-guided therapy. We develop devices using magnetohydrodynamic physics for hemodynamic monitoring.

Digital Public Health

We use social media big data and IoT smartphone devices to measure public health reactions to outbreaks including MERS-CoV, H7N9, Ebola, Zika virus, etc.