Cohort 1 (2019)
Miniaturising ultrasonic scalpels for robotic surgery
Abdul Hadi Chibli graduated in June 2019 from the University of Glasgow with an MEng in Mechatronics Engineering, having completed his final year project on the Simulation of a Robotic Worm. He is currently pursuing his PhD as part of the CDT in Future Ultrasonic Engineering and the Centre for Medical and Industrial Ultrasonics. His research project involves applying ultrasonic principles and technology to miniaturize ultrasonic surgical instruments for robotically assisted surgical platforms. Abdul Hadi also gained valuable experience during a two-month placement with CeramTec as an R&D Intern, where he designed and simulated medical ultrasonic devices. He has published three conference papers to date and is currently preparing additional papers. He is a Student Member of IEEE and an Associate Member of the UK IMechE.
This project will explore both sensing and surgical procedures with robotic deployment. For sensing, it will first identify one or a small number of relevant potential medical procedures. It will then consider the operating frequency and ultrasonic transducer configurations that may be appropriate, taking into account realistic ultrasonic device capabilities, the parameters associated with robotic deployment, and the detailed workflow of the medical procedures.
A viable prototype configuration will be designed and physical implementation and demonstration will follow. The sensing capabilities will also take into account the need for integration with ultrasonically-actuated surgical tools. Work in that area will begin with a review of tools for a range of procedures, focusing particularly on the potential of miniaturisation for intracorporeal robotic deployment, and the relationship with ultrasonic sensing.
A small number of relevant potential medical procedures will be identified. One or a small number of ultrasonically-actuated surgical tools will be developed, using design and prototyping approaches established in other projects and exciting new piezoelectric materials with the potential to reduce size and weight and increase drive efficiency. These will be demonstrated with robotic deployment in realistic simulations using artificial materials and ex vivo tissue, including complete animal cadavers. The final key phase of the project will take the understanding and physical implementations for sensing and surgery and combine them into a single demonstration system, where the ultrasonically-actuated surgery will be monitored and controlled in a simple manner with information drawn from ultrasound sensing and other sources.
Primary Supervisor (University of Glasgow): Prof. Sandy Cochran
Secondary Supervisor (University of Strathclyde): Prof. Tony Gachagan
Technology Critical Metal Recycling using Ultrasonics and Catalytic Etchants
Aseptium is a company that specialises in technologies for cleaning of complex surgical instruments. One of the core technologies used in Aseptium’s machines is ultrasonic cleaning. In this process Instruments are submerged in a liquid and energy is delivered to the surfaces of the instrument in the form of ultrasonic waves that create cavitation bubbles within the liquid volume. When these bubbles collapse shockwaves are generated that remove contamination from adjacent surfaces. Effectiveness of ultrasonic cleaning depends on the uniformity of bubble formation within the entire volume of the cleaning tank, the energy released during bubble collapse as well as bubbles ability to penetrate into intricate details of the surgical instruments. This project: A proposed study would investigate Aseptium’s current ultrasonic cleaning system from several perspectives. 1. Measuring ultrasonic activity within the volume and potentially providing feedback to the control system or ultrasonic generator. 2. Create a three dimensional map of cavitation activity in the cleaning tank and identify potential lower activity area, their size and potential impact on cleaning ability. 3. Analyse the nature of the bubble formation and shockwaves within the tank and their ability to remove contamination from instruments. 4.Investigate penetration capability of ultrasonic bubbles into the difficult to clean elements (crevices, holes, joints, channels etc). The project will be undertaken in collaboration with the Cavitation Research Laboratory, Medical and Industrial Ultrasonics, School of Engineering UoG. Research infrastructure includes state-of-the-art high speed cameras, for imaging rapid acoustic cavitation bubble dynamics, and characterising the interaction with instrumentation. Novel in-house developed acoustic detection devices will be adapted and utilised for monitoring, quantification and mapping capabilities. The researcher, under Aseptium’s guidance, would gain thorough understanding on cleaning processes and its criticality to healthcare delivery, as well as the key variables for industrial ultrasonic cleaning systems, including through fundamental research into the subject. Aseptium will provide the ultrasonic system and tests to measure cleaning ability as well as all necessary working knowledge to perform the experiments.
Primary Supervisor (University of Glasgow): Dr. Paul Prentice
Secondary Supervisor (University of Strathclyde): Dr. Richard O’Leary
Project External Partner: Aseptium
Automation for Patient Screening
Ultrasound is a widely utilized clinical diagnostic modality that presents significant untapped opportunities in the market today. The burden of ensuring adherence to the guidelines, producing diagnostically relevant imagery and interpreting them is placed entirely on the operator. This project leverages AI and advanced Imaging techniques to maximize the potential of ultrasound equipment, reducing the burden by automating many common abdominal procedures.
In a collaborative effort between Canon and FUSE CDT, Alistair has used his extensive clinical knowledge and experience to explore many advanced AI solutions to diagnostic challenges, including automation and workflow optimization, from an initial focus on image-based machine learning detection to a fully featured abdominal assistive navigation technology. This allows AI to reduce the burden on the user by providing assistive guidance in how to move the probe to collect of anatomically relevant cross sections, not only reducing user skill requirement but improving scan speed, accuracy, and adherence to protocol by automatically collecting the correct clinical planes.
Being deeply embedded into the host company has allowed Alistair to be a part of a corporate research team, directly responding to the corporate sponsors’ requirements while maintaining links to the university to allow to rapidly prototype proof of concept tools using university resources.
Primary Supervisor (University of Strathclyde): Prof Gordon Dobie
Secondary Supervisor (University of Glasgow): Dr. Kevin Worrall
Project External Partner: Canon Medical Research Europe