title: Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution.
abstract: We propose two strategies to improve the quality of tractography results computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both methods are based on the same PDE framework, defined in the coupled space of positions and orientations, associated with a stochastic process describing the enhancement of elongated structures while preserving crossing structures. In the first method we use the enhancement PDE for contextual regularization of a fiber orientation distribution (FOD) that is obtained on individual voxels from high angular resolution diffusion imaging (HARDI) data via constrained spherical deconvolution (CSD). Thereby we improve the FOD as input for subsequent tractography. Secondly, we introduce the fiber to bundle coherence (FBC), a measure for quantification of fiber alignment. The FBC is computed from a tractography result using the same PDE framework and provides a criterion for removing the spurious fibers. We validate the proposed combination of CSD and enhancement on phantom data and on human data, acquired with different scanning protocols. On the phantom data we find that PDE enhancements improve both local metrics and global metrics of tractography results, compared to CSD without enhancements. On the human data we show that the enhancements allow for a better reconstruction of crossing fiber bundles and they reduce the variability of the tractography output with respect to the acquisition parameters. Finally, we show that both the enhancement of the FODs and the use of the FBC measure on the tractography improve the stability with respect to different stochastic realizations of probabilistic tractography. This is shown in a clinical application: the reconstruction of the optic radiation for epilepsy surgery planning.
authors.affiliation: Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
authors.forename: J M
authors.initials: JM
authors.lastname: Portegies
authors.affiliation: Athena Project-Team, INRIA Sophia Antipolis-Méditerranée, France.
authors.forename: R H J
authors.initials: RH
authors.lastname: Fick
authors.affiliation: Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
authors.forename: G R
authors.initials: GR
authors.lastname: Sanguinetti
authors.affiliation: Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands; Academic Center for Epileptology Kempenhaeghe & Maastricht UMC+, Heeze, The Netherlands.
authors.forename: S P L
authors.initials: SP
authors.lastname: Meesters
authors.affiliation: Athena Project-Team, INRIA Sophia Antipolis-Méditerranée, France; Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada.
authors.forename: G
authors.initials: G
authors.lastname: Girard
authors.affiliation: Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
authors.forename: R
authors.initials: R
authors.lastname: Duits
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D004827:Epilepsy / Q000473:pathology / Q000601:surgery*; D005189:False Positive Reactions; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D011336:Probability; D013269:Stochastic Processes; D066127:White Matter / Q000473:pathology