DT-MRI Streamsurfaces Revisited

In: IEEE Transactions on Visualization and Computer Graphics (2019), 25:1
 

Abstract

DT-MRI streamsurfaces, defined as surfaces that are everywhere tangential to the major and medium eigenvector fields, have been proposed as a tool for visualizing regions of predominantly planar behavior in diffusion tensor MRI. Even though it has long been known that their construction assumes that the involved eigenvector fields satisfy an integrability condition, it has never been tested systematically whether this condition is met in real-world data. We introduce a suitable and efficiently computable test to the visualization literature, demonstrate that it can be used to distinguish integrable from nonintegrable configurations in simulations, and apply it to whole-brain datasets of 15 healthy subjects. We conclude that streamsurface integrability is approximately satisfied in a substantial part of the brain, but not everywhere, including some regions of planarity. As a consequence, algorithms for streamsurface extraction should explicitly test local integrability. Finally, we propose a novel patch-based approch to streamsurface visualization that reduces visual artifacts, and is shown to more fully sample the extent of streamsurfaces.

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Zusätzliches Material

Bibtex

@ARTICLE{ankele:vis18,
    author = {Ankele, Michael and Schultz, Thomas},
     title = {DT-MRI Streamsurfaces Revisited},
   journal = {IEEE Transactions on Visualization and Computer Graphics},
    volume = {25},
    number = {1},
      year = {2019},
  abstract = {DT-MRI streamsurfaces, defined as surfaces that are everywhere tangential to the major and medium
              eigenvector fields, have been proposed as a tool for visualizing regions of predominantly planar
              behavior in diffusion tensor MRI. Even though it has long been known that their construction assumes
              that the involved eigenvector fields satisfy an integrability condition, it has never been tested
              systematically whether this condition is met in real-world data. We introduce a suitable and
              efficiently computable test to the visualization literature, demonstrate that it can be used to
              distinguish integrable from nonintegrable configurations in simulations, and apply it to whole-brain
              datasets of 15 healthy subjects. We conclude that streamsurface integrability is approximately
              satisfied in a substantial part of the brain, but not everywhere, including some regions of
              planarity. As a consequence, algorithms for streamsurface extraction should explicitly test local
              integrability. Finally, we propose a novel patch-based approch to streamsurface visualization that
              reduces visual artifacts, and is shown to more fully sample the extent of streamsurfaces.}
}