title: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain.
abstract: This paper introduces neurite orientation dispersion and density imaging (NODDI), a practical diffusion MRI technique for estimating the microstructural complexity of dendrites and axons in vivo on clinical MRI scanners. Such indices of neurites relate more directly to and provide more specific markers of brain tissue microstructure than standard indices from diffusion tensor imaging, such as fractional anisotropy (FA). Mapping these indices over the whole brain on clinical scanners presents new opportunities for understanding brain development and disorders. The proposed technique enables such mapping by combining a three-compartment tissue model with a two-shell high-angular-resolution diffusion imaging (HARDI) protocol optimized for clinical feasibility. An index of orientation dispersion is defined to characterize angular variation of neurites. We evaluate the method both in simulation and on a live human brain using a clinical 3T scanner. Results demonstrate that NODDI provides sensible neurite density and orientation dispersion estimates, thereby disentangling two key contributing factors to FA and enabling the analysis of each factor individually. We additionally show that while orientation dispersion can be estimated with just a single HARDI shell, neurite density requires at least two shells and can be estimated more accurately with the optimized two-shell protocol than with alternative two-shell protocols. The optimized protocol takes about 30 min to acquire, making it feasible for inclusion in a typical clinical setting. We further show that sampling fewer orientations in each shell can reduce the acquisition time to just 10 min with minimal impact on the accuracy of the estimates. This demonstrates the feasibility of NODDI even for the most time-sensitive clinical applications, such as neonatal and dementia imaging.
authors.affiliation: Department of Computer Science & Centre for Medical Image Computing, University College London, UK. g.zhang@cs.ucl.ac.uk
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.forename: Torben
authors.initials: T
authors.lastname: Schneider
authors.forename: Claudia A
authors.initials: CA
authors.lastname: Wheeler-Kingshott
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Alexander
mesh_terms: D000328:Adult; D001921:Brain / Q000166:cytology*; D001931:Brain Mapping / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D016501:Neurites / Q000648:ultrastructure*; D059906:Neuroimaging / Q000379:methods*