title: Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data.
abstract: Diffusion-weighted imaging can potentially be used to infer the connectivity of the human brain in vivo using fibre-tracking techniques, and is therefore of great interest to neuroscientists and clinicians. A key requirement for fibre tracking is the accurate estimation of white matter fibre orientations within each imaging voxel. The diffusion tensor model, which is widely used for this purpose, has been shown to be inadequate in crossing fibre regions. A number of approaches have recently been proposed to address this issue, based on high angular resolution diffusion-weighted imaging (HARDI) data. In this study, an experimental model of crossing fibres, consisting of water-filled plastic capillaries, is used to thoroughly assess three such techniques: constrained spherical deconvolution (CSD), super-resolved CSD (super-CSD) and Q-ball imaging (QBI). HARDI data were acquired over a range of crossing angles and b-values, from which fibre orientations were computed using each technique. All techniques were capable of resolving the two fibre populations down to a crossing angle of 45 degrees , and down to 30 degrees for super-CSD. A bias was observed in the fibre orientations estimated by QBI for crossing angles other than 90 degrees, consistent with previous simulation results. Finally, for a 45 degrees crossing, the minimum b-value required to resolve the fibre orientations was 4000 s/mm(2) for QBI, 2000 s/mm(2) for CSD, and 1000 s/mm(2) for super-CSD. The quality of estimation of fibre orientations may profoundly affect fibre tracking attempts, and the results presented provide important additional information regarding performance characteristics of well-known methods.
authors.affiliation: Brain Research Institute, Melbourne, Australia.
authors.forename: J-Donald
authors.initials: JD
authors.lastname: Tournier
authors.forename: Chun-Hung
authors.initials: CH
authors.lastname: Yeh
authors.forename: Fernando
authors.initials: F
authors.lastname: Calamante
authors.forename: Kuan-Hung
authors.initials: KH
authors.lastname: Cho
authors.forename: Alan
authors.initials: A
authors.lastname: Connelly
authors.forename: Ching-Po
authors.initials: CP
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity