Abstract #0010

Improved tractography by modelling sub-voxel fibre patterns using asymmetric fibre orientation distributions

Matteo Bastiani¹, Michiel Cottaar¹, Krikor Dikranian², Aurobrata Ghosh³, Hui Zhang³, Daniel C. Alexander³, Timothy Behrens¹, Saad Jbabdi¹, and Stamatios N. Sotiropoulos¹

¹FMRIB Centre, University of Oxford, Oxford, United Kingdom, ²Department of Anatomy & Neurobiology, Washington University, St. Louis, MO, United States, ³Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom

Fiber bundles can cross or kiss, bend or fan within a single diffusion MRI (dMRI) voxel. Given the limited dMRI resolution and the inherent central symmetry in the measurements, these sub-voxel patterns cannot be distinguished by only using the voxel-wise signal. These asymmetric fibre patterns can be distinguished once information from neighbouring voxels is pooled together. We propose a direct estimation of asymmetric fiber orientation distributions (aFODs) based on neighbourhood-wise constrained spherical deconvolution that is capable of inferring sub-voxel patterns. We also propose a tractography algorithm based on the estimated aFODs and we assess performance using real histological fibre patterns.

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