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NGCM-0074: Fast, large scale optimisation algorithms for tomographic image reconstruction.

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Tomographic x-ray imaging techniques can take volumetric images of the inside of a patient or an object. To achieve this, x-ray projection images are collected from the object at different orientations and these projections are then used to compute a volumetric representation of the object’s internal x-ray attenuation profile. Increasingly, scientific and industrial tomographic imaging applications require the use of ever-larger datasets as they increasingly use larger and higher resolution detectors and/or use increasing numbers of projections to scan the object with the required resolution. Furthermore, the need to discern ever-finer detail within an object leads to an increase in the required resolution of the reconstructed volume. If this is paired with the use of non-standard tomographic scanning trajectories, then the filtered backprojection algorithm, which remains the primary workhorse for the tomographic reconstruction of large datasets, is no longer applicable. To address these challenges, this project will develop novel algorithms that are tailored to large scale tomographic reconstruction. The algorithms will be based on the latest developments in the field of compressed sensing and randomised algorithms. The key features will be that the algorithms will need to be able to deal efficiently with the big data challenges. This will be achieved by developing algorithms that run efficiently on modern high performance computing infrastructures, such as distributed computer networks and GPU processors, where each computing node does not have fast access to the entire dataset at once and where communication between different nodes is relatively slow.

Joining Dr Blumensath and his team at the University of Southampton’s µ-VIS volumetric imaging centre (http://www.southampton.ac.uk/muvis/index.page), you will be working on the development of world leading x-ray technology. X-ray computed tomography can take volumetric images of the inside of a patient or an object. Increasingly, scientific and industrial tomographic imaging applications require the use of ever-larger datasets as they increasingly use larger and higher resolution detectors and/or use increasing numbers of projections to scan the object with the required resolution. Furthermore, the need to discern ever-finer detail within an object leads to an increase in the required resolution of the reconstructed volume. If this is paired with the use of non-standard tomographic scanning trajectories, then the filtered backprojection algorithm, which remains the primary workhorse for the tomographic reconstruction of large datasets, is no longer applicable. To address these challenges, this project will develop novel algorithms that are tailored to large-scale tomographic reconstruction. The algorithms will be based on the latest developments in the field of compressed sensing and randomised algorithms. The key features will be that the algorithms will need to be able to deal efficiently with large datasets and with general tomographic trajectories. This will be achieved by developing algorithms that run efficiently on modern high performance computing infrastructures, such as distributed computer networks and graphical processing units (GPUs), where each computing node does not have fast access to the entire dataset at once and where communication between different nodes is relatively slow. You will have access to high end computing facilities, including the University’s supercomputer cluster and dedicated high specification volumetric image processing facilities. You will be hosted in the Faculty of Engineering’s Signal Processing and Control group and work closely with experts from the µ-VIS x-ray imaging lab. µ-VIS is one of the world’s leading x-ray facilities. It has 6 complementary CT systems and lab members have extensive experience using synchrotron facilities. The ideal candidate has an interest in image processing and applied mathematics and good programming skills.

If you wish to discuss any details of the project informally, please contact Thomas Blumensath, Signal Processing and Control research group, Email: Tel: +44 (0) 2380 59 3224.

This project is run through participation in the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling (http://ngcm.soton.ac.uk). For details of our 4 Year PhD programme, please see http://www.findaphd.com/search/PhDDetails.aspx?CAID=331&LID=2652

For a details of available projects click here http://www.ngcm.soton.ac.uk/projects/index.html

Visit our Postgraduate Research Opportunities Afternoon to find out more about Postgraduate Research study within the Faculty of Engineering and the Environment: http://www.southampton.ac.uk/engineering/news/events/2016/02/03-discover-your-future.page

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

Research output data provided by the Research Excellence Framework (REF)

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