This project combines diffuse correlation spectroscopy tomography with ultrasound tomography to develop a non-invasive approach to brain imaging. By integrating optical measurements of cerebral blood flow with ultrasound-derived structural information, the system aims to map brain perfusion and provide anatomical context for interpreting functional changes. Advanced computational methods will support image reconstruction and data integration, with the goal of improving localisation and monitoring changes associated with neurological conditions.

This project focuses on integrating artificial intelligence (AI) with wearable sensor data to enable real-time, non-invasive diagnosis of blood flow and oxygenation. By leveraging advanced machine learning algorithms, the system will analyse the optical sensing data to identify patterns and anomalies associated with various physiological conditions. AI will enhance the accuracy of the data interpretation, providing a more reliable and efficient tool for healthcare monitoring.

This project aims to develop a fibre-optic ductoscopy system for the early detection of ductal carcinoma in situ (DCIS) by integrating optical sensing and soft robotic technology. Combined with a soft robotic platform, the system offers minimally invasive, real-time imaging with high precision. The goal is to enhance breast cancer diagnostics, reduce the need for biopsies, and enable earlier intervention, ultimately improving patient outcomes.