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Abstract #2472

Multi-tissue log-domain intensity and inhomogeneity normalisation for quantitative apparent fibre density

Thijs Dhollander1,2, Rami Tabbara2, Jonas Rosnarho-Tornstrand3,4, J-Donald Tournier3,4, David Raffelt2, and Alan Connelly2
1Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia, 2Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia, 3Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 4Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Multi-tissue constrained spherical deconvolution of diffusion MRI data yields white matter fibre orientation distributions, from which a quantitative metric of apparent fibre density can be obtained. Unlike most other diffusion MRI models, this fibre density metric is directly proportional to the diffusion-weighted signal magnitude, and thus intensity normalisation and bias field correction are needed to compare it between subjects in a study. Here we propose an intensity and inhomogeneity correction algorithm for multi-tissue constrained spherical deconvolution results, estimating a bias field and global normalisation in the log-domain. It outperforms a previously proposed approach that did not operate in the log-domain.

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