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Abstract #3541

Bias Field Correction and Intensity Normalisation for Quantitative Analysis of Apparent Fibre Density

David Raffelt1, Thijs Dhollander1, J-Donald Tournier2, Rami Tabbara1, Robert E Smith1, Eric Pierre1, and Alan Connelly1

1Florey Institute of Neuroscience, Melbourne, Australia, 2Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, Kings College London, London, United Kingdom

Apparent Fibre Density (AFD) is a measure derived from un-normalised fibre orientation distributions. To make AFD quantitative across subjects, images need to be intensity normalised and bias field corrected. Here we present a fast and robust approach to simultaneous bias field correction and intensity normalisation by exploiting tissue compartment maps derived from multi-tissue constrained spherical deconvolution. We performed simulations to show that the method can accurately recover a ground truth bias field, while also demonstrating qualitative results on in vivo data.

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