Soft Robotics for Medical Imaging and Robotic Surgery

The UCL Department of Medical Physics and Biomedical Engineering is inviting applications for a studentship in the area of soft robotics for medical imaging and surgical robotics. The studentship is funded by the Engineering and Physical Sciences Research Council (EPSRC) doctoral training partnership programme.

Studentship Description
Robotic-assisted partial nephrectomy (RAPN) is a surgical operation in which part of a kidney is removed, typically due to the presence of a mass. Pre-operative and intraoperative imaging techniques are used to identify and outline the target mass, and thus the margins of the resection area on the kidney surface. Drop-in ultrasound probes are used to acquire intraoperative images: the probe is inserted through a trocar port, grasped with a robotic-assisted laparoscopic gripper and swiped on the kidney surface. Multiple swipes are performed to define the resection area. This is marked swipe by swipe using an electrocautery tool. During this procedure the probe often requires repositioning because of slippage from the target organ surface. Furthermore, the localization can be inaccurate when the target mass is in particularly hard to reach locations, and thus kidney repositioning could be required. A highly skilled surgeon is typically required to successfully perform this pre-operatory procedure.

In the Surgical Robot Vision (SRV) Group a novel soft robotic solution has been developed for the navigation of drop-in ultrasound probes in RAPN: the use of pneumatically attachable flexible rails (PAF rails) to enable swift, effortless, and accurate track-guided scanning of the kidney. The proposed system attaches on the kidney side surface with the use of a series of bio-inspired vacuum suckers. The same system can be customised to be used in other similar procedures e.g. partial hepatectomy. The PAF rail system has also been investigated as a soft robotic organ retractor and with the support of clinicians from Royal Free Hospital we are now moving towards pre-clinical trials.

The aim of this PhD studentship is to develop novel soft robotic solutions to support medical imaging and surgical robotics, building on the expertise developed within the SRV group. The candidate will design, prototype and test soft robotic systems and sensors, including but not limited to the PAF rails system and derivative systems of it, paving the way for their translation into clinics. The candidate will also investigate soft sensing solution to support multi-imaging modalities. In collaboration with other researchers within the WEISS centre and the wider UCL network of partner hospitals, the candidate will investigate how to integrate the proposed system in clinical practice, liaising with clinicians and engineers as well as with industrial partners to conduct testing on phantoms as well as ex vivo and in vivo testing.

The closing date for applications is 31 May 2020, and the student must be available to start on 28 September 2020. Interviews will be conducted in May/June 2020.

Applicants must have, or expect to obtain, a UK first class or 2:1 honours degree (or equivalent international qualifications or experience) in an appropriate technical subject.

Applicants must have a clear interest in robotics for healthcare, medical imaging and clinical translation of medical robotic devices.
The applicant would be expected to have the following essential skills:
Well-developed experimental skills and familiarity with working safely and cleanly in a laboratory; a sufficient level of mathematical and numerical skills; experience in computer programming for signal analysis and data processing (e.g. in MATLAB or similar); capable of creative and critical thinking; in possession of excellent writing and oral communication skills; capable of self-management and good working habits; used to taking initiative; capable of working both independently and collaboratively. Experience with hardware-software engineering and integration.

The following skills are desirable but not essential:
Experience with embedded systems (microcontrollers/microprocessors programming); experience with rapid prototyping techniques such as 3D printing, milling, laser cutting and silicone moulding; a working knowledge of ROS and Phyton. A working knowledge of medical imaging. A working knowledge of CAD (Solidworks Toolkit preferable).

Eligibility
If you have any project-specific queries, please contact Dr. Agostino Stilli ([Email Address Removed]). Applications (including a covering letter, CV and names of two referees) should be sent to Miss Mohini Nair ([Email Address Removed]). Any informal enquiries about the position should be directed to Dr. Agostino Stilli ([Email Address Removed]).