Prof Zion Tse


Research Funding

Current Funded Research Projects

AI-guided Prostate Biopsy

The AMS Professorship will enable Prof. Tse's team to clinically validate next-generation interventional technology for affordable image fusion with a small footprint and translate it to clinical practice. Zion’s planned project, a collaboration between his lab and Addenbrooke’s Hospital, will aim to improve freehand transperineal prostate cancer biopsy by developing an integrated 3D MRI-US image fusion system built upon state-of-the-art AI techniques. This system will help clinicians precisely navigate needles to lesions in the prostate for more effective diagnosis and treatment.

MRI-guided Focal Laser Ablation for Prostate Cancer Treatment

Prostate cancer is one of the most common malignancies in males and has now become the second leading cause of cancer mortality. Prostate cancer diagnosis has increased from 3.9% to 8.2% of the population in the past decade. Approximately 52,300 new cases of prostate cancer are diagnosed in the United Kingdom every year, which is more than 140 every day. In this study, a robotic platform used for MRI-guided prostate therapy, including both biopsy and ablation, will be developed and validated. Compared with all the listed MR-safe robot platforms, the presented design will have a compact size, allowing it to be placed inside the scanner quickly. Moreover, the use of pneumatic stepper actuators will reduce the affection of EMI generated by piezoelectric motors and all the other parts are made of plastic, making the whole system to be MR safe.

Remote Vital Signs Monitor for Infection Control or Fall Prevention

Recent miniaturisation developments in electronic systems have resulted in a wearable technology boom. This in turn has led to an increase in both vital sign monitoring and research into non-invasive and continuous monitoring methods. Various studies have shown the feasibility of using seismocardiogram (SCG) in heart rate variation (HRV) analysis and diagnostic purposes. This study aims to build upon the research done on SCG through development of a novel, real time Android based system which can calculate the heart rate and the respiratory rate of patients.

Cost-effective Lung Biopsy with Intraoperative Electrical Impedance Sensing and Artificial Intelligence Navigation

Funding source: NIH National Institutes of Health - USA
Start: 01-11-2022  /  End: 31-10-2023
Amount: £25,611

Lung cancer is the second most common cancer in both men and women. More than one million lung cancer cases are diagnosed worldwide each year. It has the highest death rate among all types of cancers in the United States and worldwide. Early detection with higher yield tissue diagnosis as well as an accurate localization during lung interventions may help reduce the impact, death rate, and overall population cost of lung cancer. Accurate and timely clinical information facilitates patient-specific therapy decisions, resulting improved clinical outcomes. Engineering approaches that are low cost and also Accurate localization of biopsy devices alongside of verification that biopsy needle is within solid abnormal tissue (and not normal non-target lung) can signi?cantly improve the accuracy of the diagnosis, which further helps clinicians make the optimal decision via a lung treatment decision tree.

Smartphone Application For Effective Prostate Cancer Screening With Machine Learning Enhanced PSA-density Measurement

Prostate cancer (PCa) is the commonest male cancer. Prostate-specific antigen (PSA) testing is the first-line investigation used for referral to secondary care. Less than half of the 120,000 patients/year referred in the UK are ultimately diagnosed with PCa, highlighting the inefficiencies in the system, including the use of MRI as an expensive resource and biopsy as an invasive procedure. A common reason for raised PSA levels is benign gland overgrowth, and therefore PSA-density corrects for overgrowing gland volume, and therefore has utility for and indicating the presence of clinically significant cancer. Ultrasound (US) can measure gland volume provide such information, however, currently thisUS is currently performed in secondary care by specialized specialised practitioners, which increases costs and may delay cancer pathways. Making US volume calculations automated, cheap, and potentially available in primary care would avoid such limitations. The aims of this project are to develop a prototype device for automated US measurement of prostate volume and validate performance in a patient cohort.