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Fiber visualization for preoperative glioma assessment: Tractography versus local connectivity mapping

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  • Thomas Schult
  • Till-Karsten Hauser
  • Uwe Klose
  • Helene Hurth
  • Hans-Heino Ehricke

Abstract

In diffusion MRI, the advent of high angular resolution diffusion imaging (HARDI) and HARDI with compressed sensing (HARDI+CS) has led to clinically practical signal acquisition techniques which allow for the assessment of white matter architecture in routine patient studies. However, the reconstruction and visualization of fiber pathways by tractography has not yet been established as a standard methodology which can easily be applied. This is due to various algorithmic problems, such as a lack of robustness, error propagation and the necessity of fine-tuning parameters depending on the clinical question. In the framework of a clinical study of glioma patients, we compare two different whole-brain tracking methods to a local connectivity mapping approach which has recently shown promising results in an adaptation to diffusion MRI. The ability of the three methods to correctly depict fiber affection is analyzed by comparing visualization results to representations of local diffusion profiles provided by orientation distribution functions (ODFs). Our results suggest that methods beyond fiber tractography, which visualize local connectedness rather than global connectivity, should be evaluated further for pre-surgical assessment of fiber affection.

Suggested Citation

  • Thomas Schult & Till-Karsten Hauser & Uwe Klose & Helene Hurth & Hans-Heino Ehricke, 2019. "Fiber visualization for preoperative glioma assessment: Tractography versus local connectivity mapping," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-22, December.
  • Handle: RePEc:plo:pone00:0226153
    DOI: 10.1371/journal.pone.0226153
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    References listed on IDEAS

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    1. José V Manjón & Pierrick Coupé & Luis Concha & Antonio Buades & D Louis Collins & Montserrat Robles, 2013. "Diffusion Weighted Image Denoising Using Overcomplete Local PCA," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
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