title: Bingham-NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI.
abstract: This paper presents Bingham-NODDI, a clinically-feasible technique for estimating the anisotropic orientation dispersion of neurites. Direct quantification of neurite morphology on clinical scanners was recently realised by a diffusion MRI technique known as neurite orientation dispersion and density imaging (NODDI). However in its current form NODDI cannot estimate anisotropic orientation dispersion, which is widespread in the brain due to common fanning and bending of neurites. This work proposes Bingham-NODDI that extends the NODDI formalism to address this limitation. Bingham-NODDI characterises anisotropic orientation dispersion by utilising the Bingham distribution to model neurite orientation distribution. The new model estimates the extent of dispersion about the dominant orientation, separately along the primary and secondary dispersion orientations. These estimates are subsequently used to estimate the overall dispersion about the dominant orientation and the dispersion anisotropy. We systematically evaluate the ability of the new model to recover these key parameters of anisotropic orientation dispersion with standard NODDI protocol, both in silico and in vivo. The results demonstrate that the parameters of the proposed model can be estimated without additional acquisition requirements over the standard NODDI protocol. Thus anisotropic dispersion can be determined and has the potential to be used as a marker for normal brain development and ageing or in pathology. We additionally find that the original NODDI model is robust to the effects of anisotropic orientation dispersion, when the quantification of anisotropic dispersion is not of interest.
authors.affiliation: Department of Computer Science & Centre for Medical Image Computing, University College London, UK. Electronic address: maira.tariq.11@ucl.ac.uk.
authors.forename: Maira
authors.initials: M
authors.lastname: Tariq
authors.affiliation: NMR Research Unit, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, UK.
authors.forename: Torben
authors.initials: T
authors.lastname: Schneider
authors.affiliation: Department of Computer Science & Centre for Medical Image Computing, University College London, UK.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Alexander
authors.affiliation: NMR Research Unit, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, UK; Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy.
authors.forename: Claudia A
authors.initials: CA
authors.lastname: Gandini Wheeler-Kingshott
authors.affiliation: Department of Computer Science & Centre for Medical Image Computing, University College London, UK.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000328:Adult; D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000166:cytology* / Q000000981:diagnostic imaging; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D016501:Neurites / Q000648:ultrastructure*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
keywords: Anisotropic orientation dispersion; Bingham distribution; Diffusion MRI; NODDI; Neurite morphology