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Details of Grant 

EPSRC Reference: EP/F01144X/1
Title: HPC Software for Medical Imaging
Principal Investigator: Atkinson, Dr D
Other Investigators:
Researcher Co-Investigators:
Professor JA Schnabel
Project Partners:
Department: Medical Physics and Biomedical Eng
Organisation: UCL
Scheme: Standard Research
Starts: 01 October 2007 Ends: 31 March 2009 Value (£): 173,695
EPSRC Research Topic Classifications:
High Performance Computing Medical science & disease
EPSRC Industrial Sector Classifications:
Healthcare Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
16 Apr 2007 HPC Software Development (Science) Announced
Summary on Grant Application Form
The performance of computers with a single core , i.e. a traditional CPU, is not expected to continue increasing at the same rate as in the past. To boost overall computer performance, manufacturers now provide multi-core processors. For the development of faster software, programmers are going to be forced to make use of multi-core technology. The power of the mass market means that there are now two relatively cheap hardware configurations each with about 100 cores. These are computer clusters where PCs are networked together, and, graphics cards. The performance of graphics cards has recently been outstripping that of CPUs. Graphics cards have the added advantages of being cheap and self-contained.In medical imaging, the alignment or registration of images is an important step in clinical image analysis. In diseases such as Alzheimer's or other forms of dementia, the brain shrinks over an extended period of time. Over shorter time scales these changes are not obvious when looking at two images side-by-side. Registration aligns images by accounting for differences in the patient position and scanner variations. It also provides a measure of local changes in tissue volume. These algorithms take hours to run, longer than the time a patient is in a scanner. If the data processing could be complete with a few minutes, more detailed follow-up scanning could be possible.Registration is also used to combine large numbers of datasets to determine normal and abnormal anatomy. This is often called atlas building. The creation of atlases needs large amounts of memory and this currently limits the number of datasets that can be included. Building an atlas is well suited to a computer cluster. Once created, if an atlas could be registered with a patient's image whilst they were still in the scanner, it could provide information on-the-spot about abnormal anatomy in the patient. Some of the newer Magnetic Resonance Imaging (MRI) techniques such as diffusion weighted imaging and functional MRI provide detailed information about nerve connections in the brain or show up thoughts . The processing of this data requires images to be in alignment, again, if this could be complete in minutes, diagnosis would be enhanced. This proposal aims to develop algorithms using high-level languages for use on graphics cards or computer clusters. Much of the previous work in this area has required specialised knowledge of the hardware and considerable programming skills. By using the newer high-level languages, we will be able to concentrate more on the applications and problems than the details of the hardware and software architecture. This lends itself to high-quality code because it can be read easily by others, checked on other systems and integrates with debugging and visualisation tools.We aim to demonstrate and evaluate registration algorithms in clinical applications that are currently not feasible due to the data processing being too slow or memory-intensive.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
Sectors submitted by the Researcher
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Project URL: http://cmic.cs.ucl.ac.uk/home/software/
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