Fuzzy Fibers: Uncertainty in dMRI Tractography

Thomas Schultz, Anna Vilanova, Ralph Brecheisen, and Gordon Kindlmann
In: Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, Springer, Sept. 2014
 

Abstract

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research.

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Bibtex

@INCOLLECTION{SchultzSciDag2014,
     author = {Schultz, Thomas and Vilanova, Anna and Brecheisen, Ralph and Kindlmann, Gordon},
      title = {Fuzzy Fibers: Uncertainty in dMRI Tractography},
  booktitle = {Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization},
     series = {Mathematics + Visualization},
       year = {2014},
      month = sep,
  publisher = {Springer},
       note = {Accepted for publication.},
   abstract = {Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive
               reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and
               uncertainty in this technique, and review strategies that afford a more reliable interpretation of
               the results. This includes methods for computing and rendering probabilistic tractograms, which
               estimate precision in the face of measurement noise and artifacts. However, we also address aspects
               that have received less attention so far, such as model selection, partial voluming, and the impact
               of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses
               for future research.}
}