This project aims to develop innovative instruments for minimally invasive surgery (MIS) equipped with sensors capable of reliably detecting blood vessels in deep tissues. This advanced technology will allow surgeons to identify arteries hidden by layers of fatty or scar tissue and support autonomous task planning in the next generation of Surgical Robots.

Currently, MIS faces limitations due to reduced visibility and lack of tactile feedback, often requiring surgeons to rely on visual cues. Although recent advancements in hyperspectral imaging and fluorescent dye injections offer some improvements, these methods require a line of sight and are restricted to surface-level tissues. Current sensorised instruments, relying on pressure or optical methods, are hindered scattering effects caused by layers of adipose or fibrotic tissue. This gap in effective deep-tissue vessel detection limits the ability of robots to plan tasks safely.

To overcome these limitations, we propose a novel Multi-Frequency Electrical Impedance Tomography (MF-EIT) approach. By using multiple electrodes and cardiosynchronous signal processing, this approach will allow deeper and more accurate detection of blood vessels, even in the presence of challenging tissues. Synchronizing the MF-EIT signals with the patient's ECG cycle and averaging over heartbeats is expected to significantly enhance the SNR, expanding the instrument's sensitive volume and enabling real-time feedback. This would enable the sensor data to be integrated into existing manual and robotic surgical workflows and track the locations of safety critical structures even as the tissue deforms during operations.

This research introduces a unique approach to addressing fundamental safety challenges inherent in MIS procedures. By developing hardware and firmware for real-time processing and potentially overlaying information onto surgical imaging, this project opens new avenues for AI-assisted scanning and autonomous robotic surgery. The sensing concept will be validated in tissue mimicking phantoms and potentially live porcine models. Later stages of the project will focus on into the STORM Lab’s da Vinci Research Kit, to contribute to the groundbreaking research into autonomy in robotic surgery.