A smart catheter for endovascular applications
Applications are invited for a full time PhD studentship co-funded by the EPSRC and an industry partner. The studentship is for a period of 3 years (36 months) starting 1st January 2020 or at a mutually agreed time after that. The successful applicant will receive an annual stipend of £17,009 plus payment of their annual full-time home tuition fees. Applicants must be eligible for home tuition fees, either through nationality, residency or other connection to the UK.
Cardiovascular, neurovascular and peripheral vascular (arterial and venous) diseases are frequently diagnosed, assessed and treated from within the circulation using a variety of endovascular techniques. The techniques are far less invasive than conventional open surgery but are hampered by technical complexity. Endovascular specialists work with catheters and guidewires at a remote distance to gain access to the diseased artery, often negotiating tortuous, complex and diseased vessels in the process. There is no direct visualisation or feedback from the operative field and procedures are performed using frequent 2D fluoroscopic imaging where the guidewire, and (sometimes) the catheter, are visible. Such technical difficulties increase the risk of complications and reduce the applicability of endovascular techniques. While the approach offers significant benefit to the patient, the operator is vulnerable to high doses of ionising radiation.
This PhD project is to further develop a smart catheter device and test on 3D phantoms and in an environment similar to human body. This project will also engage appropriate manufacturing processes. The proposed smart catheter system offers greater perception of the working site using tactile information, pre-operative scan data and visualisation techniques. Previously in Brunel University, a bench top system has been built to demonstrate the feasibility on 2D phantoms. The system embedded 3 Fibre Bragg gratings (FBGs) at the tip of a standard catheter (Figure 1). The tip of the catheter is considered as a beam subjected to applied loading transients that reflect the nature of the working environment within the vasculature. FBGs placed within the catheter sleeve, coupled by this structure respond to the transients in applied loading and the time series data interpreted to discriminate events, e.g. the nature of contact with tissues, the deformation of the catheter sleeve, and the orientation of the curved tip of the guidewire within the sleeve. A similar approach is applied here where the curvature of the catheter, and wire on the axis, the axial orientation of the curved guide wire, the range of contact and the nature of contact force are discriminated in real-time in 3D using three coupled strain sensitive sensing elements. The suite of Feed-forward back propagation artificial neural networks (ANNs) has been trained successfully through both simulation and practical measurement data.
Applicants will have or expect to receive a first degree at 2:1 or above in an Engineering or Physical Sciences related discipline. A master’s degree is an advantage but not essential. Applicants with Electronics Engineering background having taken courses in Robotics, AI are strongly encouraged to apply.
How to apply
Please contact Dr. Xinli Du at firstname.lastname@example.org for an informal discussion about the project.
How to apply
Please email your application comprising all of the documents listed below by Noon on Monday 23 September 2019 to email@example.com and firstname.lastname@example.org
- Your up-to-date CV;
- A one A4 page personal statement setting out why you are a suitable candidate (i.e.: your skills and experience);
- Your degree transcripts and/or certificates;
- Name and contact details for two academic referees.
Interviews will take place in October 2019.