Diagnosing knee, shoulder and back complaints can be difficult because we currently cannot image a patient as they walk or pick up an object. The aim of this project is to develop imaging techniques that will allow us to image a patient while performing the activity that gives them pain. This project can be tailored to suit students from a broad range of disciplines and could involve robotics, engineering or computer science.
This project involves placing external sensors on a patient to measure the motion of the patient as they walk or pick up an object. This information will then be used to adjust the location of the imaging device, an Artis Zeego, on-the-fly to compensate for the patients movement and then acquire both 2D and 3D images of the patient while performing normal activities.
Examples of tasks that might be involved in this project include:
• Engineering: Developing a novel phantom which is a device that mimics a human knee or shoulder as we walk or pick up an object.
• Engineering/robotics/computer science: Developing real-time control systems and embedded programing to accept input from motion sensors and adjust image acquisition on-the-fly.
• Mathematics/computer science and engineering: Developing optimisation, guidance and control algorithms to control the trajectory of the imaging hardware.
X-Rays, CT's and MRI's are designed to acquire images of a patient in a fixed position (i.e. lying on an imaging table). However, they are not designed to image a patient while the knee is under load while they walk, or when a patient bends over to pick up an item. For many patients, their symptoms are only present while moving. We have negotiated access to the real-time control system of an Artis Zeego C-arm imaging device which will allow us to change to position of the imaging device as a patient moves. In this project we will reprogram the device to acquire images of a patient as they move or pick up an item.
This opportunity is suitable for students with a background in Medicine, Science or Engineering.
The opportunity ID for this research opportunity is 2193