Mrtrix: diffusion tractography in crossing fiber regions. In recent years, diffusion-weighted magnetic resonance imaging has attracted considerable attention due to its unique potential to delineate the white matter pathways of the brain. However, methodologies currently available and in common use among neuroscientists and clinicians are typically based on the diffusion tensor model, which has comprehensively been shown to be inadequate to characterize diffusion in brain white matter. This is due to the fact that it is only capable of resolving a single fiber orientation per voxel, causing incorrect fiber orientations, and hence pathways, to be estimated through these voxels. Given that the proportion of affected voxels has been recently estimated at 90%, this is a serious limitation. Furthermore, most implementations use simple “deterministic” streamlines tracking algorithms, which have now been superseded by “probabilistic” approaches. In this study, we present a robust set of tools to perform tractography, using fiber orientations estimated using the validated constrained spherical deconvolution method, coupled with a probabilistic streamlines tracking algorithm. This methodology is shown to provide superior delineations of a number of known white matter tracts, in a manner robust to crossing fiber effects. These tools have been compiled into a software package, called MRtrix, which has been made freely available for use by the scientific community.

References in zbMATH (referenced in 11 articles )

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  1. Jensen, Henrik G.; Lauze, François; Darkner, Sune: Information-theoretic registration with explicit reorientation of diffusion-weighted images (2022)
  2. Calimeri, Francesco; Cauteruccio, Francesco; Cinelli, Luca; Marzullo, Aldo; Stamile, Claudio; Terracina, Giorgio; Durand-Dubief, Françoise; Sappey-Marinier, Dominique: A logic-based framework leveraging neural networks for studying the evolution of neurological disorders (2021)
  3. Greene, Clint; Revill, Kate; Buetefisch, Cathrin; Rose, Ken; Grafton, Scott: Optimal fiber diffusion model restoration (2020)
  4. Duits, R.; Meesters, S. P. L.; Mirebeau, J.-M.; Portegies, J. M.: Optimal paths for variants of the 2D and 3D Reeds-Shepp car with applications in image analysis (2018)
  5. Csaba Kerepesi, Balazs Szalkai, Balint Varga, Vince Grolmusz: The Database of High Resolution Structural Connectomes and the Brain Graph Tools (2016) arXiv
  6. Fuster, Andrea; Dela Haije, Tom; Tristán-Vega, Antonio; Plantinga, Birgit; Westin, Carl-Fredrik; Florack, Luc: Adjugate diffusion tensors for geodesic tractography in white matter (2016)
  7. Wu, Xi; Yang, Zhipeng; Hu, Jinrong; Peng, Jing; He, Peiyu; Zhou, Jiliu: Diffusion-weighted images superresolution using high-order SVD (2016)
  8. Hohage, T.; Rügge, C.: A coherence enhancing penalty for diffusion MRI: regularizing property and discrete approximation (2015)
  9. Portegies, Jorg; Sanguinetti, Gonzalo; Meesters, Stephan; Duits, Remco: New approximation of a scale space kernel on SE(3) and applications in neuroimaging (2015)
  10. Prčkovska, Vesna; Andorrà, Magí; Villoslada, Pablo; Martinez-Heras, Eloy; Duits, Remco; Fortin, David; Rodrigues, Paulo; Descoteaux, Maxime: Contextual diffusion image post-processing aids clinical applications (2015) ioport
  11. Vaillancourt, Olivier; Chamberland, Maxime; Houde, Jean-Christophe; Descoteaux, Maxime: Visualization of diffusion propagator and multiple parameter diffusion signal (2015) ioport

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