About the project
This project focuses on acquiring 2 nearly simultaneous radiographic projections taken from different angles and combined with a baseline 3D data set. The extraction of 3D positional information of discrete points within a specimen on timescales beyond current capabilities.
The University of Southampton’s dedicated X-ray Computed Tomography (X-CT) centre, µ-VIS, is part of the UK’s National facility for X-CT. It houses some of the UK’s largest micro-focus CT scanning systems capable of unveiling sub-surface information from materials, components and structures.
This technique is most exciting when used for in situ experiments but there are significant real world considerations. The object needs to rotate and this affects the time associated with scan acquisition. In situ experiments are often interrupted tests, where the process is paused or slowed to allow information to be collected at a series of discrete time or load steps.
This results in an estimated interpolation of events between capture points and possible effects due to the interruptions themselves. Someone requesting imagery must decide between 3D CT, which misses temporal changes shorther than the scan time, or 2D radiography, which compromises spatially sensitive events.
This project aims to fill the gap between full 3D CT and 2D time series radiography by using pseudo stereo image capture combined with benchmark full 3D CT scans. The projection points may be tracked as the dynamic process is allowed to progress uninterrupted between an initial and final state. This yields a temporal and spatial resolution not otherwise achievable.
There will be a very strong link and interest from one of our long-term industrial partners with special interest in defence and national security. This partnership will bring a strong focus to the project, as well as opportunities for future employment. We have strong links with research and industry and the centre has an extensive list of applications.
Project supervisor
The lead supervisor is Dr Mark Mavrogordato. In this YouTube video, Mark talks about his research.
