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Abstract #1387

Non-Rigid Registration of Diffusion Weighted Images Using Fibre Orientation Distributions

David Raffelt1,2, J-Donald Tournier3,4, Jurgen Fripp5, Natasha Lepore6, Stuart Crozier2, Alan Connelly3,4, Olivier Salvado1

1The Australian E-Health Research Centre, CSIRO, Brisbane, QLD, Australia; 2Department of Biomedical Engineering, University of Queensland, Brisbane, QLD, Australia; 3Brain Research Institute, Florey Neuroscience Institutes (Austin), Melbourne, VIC, Australia; 4Department of Medicine, University of Melbourne, Melbourne, VIC, Australia; 5The Australian E-Health Research Centre, CSIRO,, Brisbane, QLD, Australia; 6Laboratory of Neuro Imaging, David Geffen School of Medicine UCLA, Los Angeles, CA, USA

Fibre Orientation Distributions (FODs) obtained from diffusion weighted imaging (DWI) provide a non-invasive method to investigate white matter structure and connectivity. In order to compare white matter between individuals, spatial normalisation is required. We propose a free form deformation registration method using a novel similarity measure that optimises directly on FODs. Our technique compared favourably with existing T1 weighted and fractional anisotropy based methods: average images were sharper indicative of more accurate spatial alignment. Unlike existing scalar or diffusion tensor based techniques, this method exploits information provided by FODs in voxels with crossing fibres, independently of how FOD are computed.