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Details of Grant 

EPSRC Reference: EP/P012841/1
Title: Robotic Assisted Imaging
Principal Investigator: Stoyanov, Dr D
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Intuitive Surgical Inc
Department: Computer Science
Organisation: UCL
Scheme: EPSRC Fellowship
Starts: 01 July 2017 Ends: 30 June 2022 Value (£): 1,239,250
EPSRC Research Topic Classifications:
Image & Vision Computing Medical Imaging
Robotics & Autonomy
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
13 Feb 2017 Eng Fellowship Interviews Feb 2017 Announced
01 Dec 2016 Engineering Prioritisation Panel Meeting 1 and 2 December 2016 Announced
Summary on Grant Application Form
The paradigm of modern surgical treatment is to reduce the invasive trauma of procedures by using small keyhole ports to enter the body. Robotic assistant systems provide tele-manipulated instruments that facilitate minimally invasive surgery by improving the ergonomics, dexterity and precision of controlling manual keyhole surgery instruments. Robotic surgery is now common for minimally invasive prostate and renal cancer procedures. But imaging inside the body is currently restricted by the access port and only provides information at visible organ surfaces which is often insufficient for easy localisation within the anatomy and avoiding inadvertent damage to healthy tissues.

This project will develop robotic assisted imaging which will exploit the autonomy and actuation capabilities provided by robotic platforms, to optimise the images that can be acquired by current surgical imaging modalities. In the context of robotic assisted surgery, now an established surgical discipline, advanced imaging can help the surgeon to operate more safely and efficiently by allowing the identification of structures that need to be preserved while guiding the surgeon to anatomical targets that need to be removed. Providing better imaging and integration with the robotic system will result in multiple patient benefits by ensuring safe, accurate surgical actions that lead to improved outcomes.

To expose this functionality, new theory, computing, control algorithms and real-time implementations are needed to underpin the integration of imaging and robotic systems within dynamic environments. Information observed by the imaging sensor needs to feed back into the robotic control loop to guide automatic sensor positioning and movement that maintains the alignment of the sensor to moving organs and structures. This level of automation is largely unexplored in robotic assisted surgery at present because it involves multiple challenges in visual inference, reconstruction and tracking; calibration and re-calibration of sensors and various robot kinematic strategies; integration with surgical workflow and user studies. Combined with the use of pre-procedural planning, robotic assisted imaging can lead to pre-planned imaging choices that are motivated by different clinical needs.

As well as having direct applications in surgery, the robotic assisted imaging paradigm will be applicable to many other sectors transformed by robotics, for example manufacturing or inspection, especially when working within non-rigid environments. For this cross sector impact to be achieved the project will build the deep theoretical and robust software platforms that are ideally suited for foundational fellowship support.
Key Findings
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