title: Tractometer: towards validation of tractography pipelines.
abstract: We have developed the Tractometer: an online evaluation and validation system for tractography processing pipelines. One can now evaluate the results of more than 57,000 fiber tracking outputs using different acquisition settings (b-value, averaging), different local estimation techniques (tensor, q-ball, spherical deconvolution) and different tracking parameters (masking, seeding, maximum curvature, step size). At this stage, the system is solely based on a revised FiberCup analysis, but we hope that the community will get involved and provide us with new phantoms, new algorithms, third party libraries and new geometrical metrics, to name a few. We believe that the new connectivity analysis and tractography characteristics proposed can highlight limits of the algorithms and contribute in solving open questions in fiber tracking: from raw data to connectivity analysis. Overall, we show that (i) averaging improves quality of tractography, (ii) sharp angular ODF profiles helps tractography, (iii) seeding and multi-seeding has a large impact on tractography outputs and must be used with care, and (iv) deterministic tractography produces less invalid tracts which leads to better connectivity results than probabilistic tractography.
authors.affiliation: Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada.
authors.forename: Marc-Alexandre
authors.initials: MA
authors.lastname: Côté
authors.forename: Gabriel
authors.initials: G
authors.lastname: Girard
authors.forename: Arnaud
authors.initials: A
authors.lastname: Boré
authors.forename: Eleftherios
authors.initials: E
authors.lastname: Garyfallidis
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Houde
authors.forename: Maxime
authors.initials: M
authors.lastname: Descoteaux
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D020407:Internet; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
keywords: ABC; Analytical Orientation Distribution Function (ODF); Average Bundle Coverage; CC; CSF; CST; Cerebral Spinal Fluid; Cg; Cingulum; Connectivity analysis; Constant Solid Angle ODF; Corpus Callosum; Corticospinal Tract; DTI; DWI; Diffusion MRI; Diffusion Tensor Imaging; Diffusion Weighted Imaging; FA; Fiber ODF; Fractional Anisotropy; GM; Grey Matter; HARDI; High Angular Resolution Diffusion Imaging; IB; IC; Invalid Bundles; Invalid Connections; NC; No Connections; ODF; Orientation Distribution Function; ROI; Region Of Interest; Runge–Kutta; SD-r6; SLF; Spherical Deconvolution of maximal SH order 6 (r6); Superior Longitudinal Fasciculus; TEND; Tensor Advection; Tractography; VB; VC; Valid Bundles; Valid Connections; Validation; WM; White Matter; a-ODF; csa-ODF; dMRI; diffusion Magnetic Resonance Imaging; fODF; maximal SH order 6; r6; rk