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Details of Grant 

EPSRC Reference: GR/S82503/01
Title: IBIM: Integrated Brain Image Modelling
Principal Investigator: Smith, Professor SM
Other Investigators:
Cootes, Professor TF Rueckert, Professor D Hill, Professor DL
Researcher Co-Investigators:
Dr W Crum Professor M Jenkinson
Project Partners:
Massachusetts General Hospital
Department: Clinical Neurology
Organisation: University of Oxford
Scheme: Standard Research (Pre-FEC)
Starts: 01 July 2004 Ends: 31 December 2007 Value (£): 786,327
EPSRC Research Topic Classifications:
Biomedical neuroscience Medical science & disease
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
High-resolution magnetic resonance (MR) brain imaging is an extremely rich source of information about brain morphology, function, development and pathology. Clinical researchers are increasingly interested in using brain image analysis to answer complex questions such as how can we use measurements of brain structure and function to better evaluate drug treatments? and how can we predict the onset of Alzheimer's disease at the earliest possible stage to initiate preventative treatments? An even more significant clinical research area is that of schizophrenia, a debilitating disease affecting around half a million people in the UK alone. The disease's causes are still poorly understood, and it is generally accepted that results from structural brain imaging are likely to be a major contributor to improving this understanding; knowing about the shapes of different structures within the brain, and how they relate to each other is expected to be hugely valuable as neuroscientists search for, e.g., brain developmental markers of schizophrenia.We intend to produce a system for analysing structural brain images which not only will automatically identify and outline each brain substructure and tissue type, but also, more importantly, will quantify how different brain structures relate to each other, in relative position, size and shape. To date, analysis techniques have tended to be been highly specialised for asking particular questions of particular diseases or patient groups and are not able to generalise well to other questions or pathologies. This proposal outlines an ambitious strategy to develop a highly robust method that gains generalisability by the integration and interaction of previously independent analysis methods including those for statistical shape analysis, modelbuilding, image registration and feature detection. This will create a novel synthesis suitable for application to many disease conditions. The approach aspires to define a new generation of generic neuroimaging analysis tools that can be applied in an unsupervised fashion. These tools will enable existing and future databases of brain images to be analysed in a hypothesis-free mode to answer questions like those above, as well as many more.
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