CLOSED - Augmented Reality for Robotic Surgery (23035)

Supervision Team: Prof. Matt Clarkson & Prof. Dan Stoyanov

A 4-year funded MPhil/PhD studentship is available in the UCL Centre for Doctoral Training (CDT) in Intelligent, Integrated Imaging in Healthcare (i4Health), and based in the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) at Charles Bell House. The studentship provides a stipend for four years and tuition fees suitable for either home or overseas students. Stipend details can be found here. 

The successful candidate will join the i4Health cohort and benefit from the activities and events organised by the CDT.

The PhD studentship is part-funded by Medtronic. 

Project background

Kidney cancer has an estimated incidence of 14,000 (UK) and 430,000 (Globally) per year, leading to a yearly mortality rate of 4,700 (UK) or 180,000 (Globally). The incidence is increasing by 2-3% per year. Compared to open surgery, minimally invasive surgery such as robotic and laparoscopic surgery benefits the patient in terms of reduced trauma and shorter hospital stays but requires specialist equipment and training. The main surgical challenges for minimally invasive surgery are to control bleeding, maintain tumour margins (don’t cut into tumour) and avoid inadvertent damage (don’t cut other organs/vessels). 

For the treatment of cancer, Robot-Assisted Surgery (RAS) has many advantages over open surgery, but it is still highly challenging and difficult to learn which increases the risk of surgical complications, major injury or even death.  Augmented Reality (AR) has been proposed as a method to guide the surgeon and reduce risk, but current registration methods are not accurate or reliable enough, so no commercial AR-enabled robotic surgery platform exists. 

Research aims

This project aims to develop novel automated registration (alignment) methods for RAS for nephrectomy. This may include some/all of the following:

Aim 1: We will employ Artificial Intelligence (AI) based approaches to extra features such as surfaces or contours from laparoscopic video and ultrasound images. In parallel, we will be regularly collecting clinical data from surgical cases. 

Aim 2: We will develop Artificial Intelligence (AI) based approaches to solve the automatic registration of pre-operative CT data to intra-operative video and utilise intra-operative ultrasound imaging. 

Aim 3: We will develop a software platform to deploy models in real-time so that they can be run in parallel to clinical systems and used for a first in human clinical feasibility study.

Aim 4: We will validate algorithms using simulations, lab-based phantom validations, and qualitative clinical evaluation with a range of surgeons.
 

Person specification & requirements

This studentship would be suitable for candidates who are:

• Passionate about delivering solutions for healthcare
• Want to work in a multi-disciplinary team
• Innovative and highly motivated
• Have previously studied a subject such as Computer Science, Medical Imaging, Biomedical Engineering, Mechanical Engineering
• Good at programming in languages such as Python, C++ or similar
• Good at mathematics

The above list is not exhaustive. Please email Prof. Matt Clarkson (m.clarkson@ucl.ac.uk), or Dr. Joao Ramalhinho (joao.ramalhinho.15@ucl.ac.uk) to discuss eligibility.

Funding

This studentship is available for home and overseas fee payers. Please see the page on UCL fee status here.

How to apply:

Application Deadline: Friday 19th July 2024

Please complete the following steps to apply.

•  Send an expression of interest and current CV to: m.clarkson@ucl.ac.uk and cdtadmin@ucl.ac.uk. Please quote Project Code: 23035 in the subject line.
• Make a formal application to via the UCL application portal. Please select the programme code Medical Imaging RRDMEISING01 and enter Project Code 23035 under ‘Name of Award 1’.

If shortlisted, you will be invited for an interview.