gms | German Medical Science

58. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie e. V. (DGNC)

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

26. bis 29.04.2007, Leipzig

Diffusion tensor imaging data in brain tumor surgery

Diffusion Tensor Imaging – Dateneinbindung in der neurochirugischen Operationsplanung von Hirntumoren

Meeting Abstract

  • corresponding author G. Schackert - Klinik und Poliklinik für Neurochirurgie, Carl Gustav Carus Universitätsklinikum der Technischen Universität Dresden
  • H. Kitzler - Abteilung für Neuroradiologie, Carl Gustav Carus Universitätsklinikum der Technischen Universität Dresden
  • W. Benger - Center for Computation & Technology at Louisiana State University (CCT/LSU), Visualization and Digital Arts Group (VIDA), Baton Rouge, Lousiana, USA
  • A. Werner - Abteilung für Neuroradiologie, Carl Gustav Carus Universitätsklinikum der Technischen Universität Dresden
  • R. von Kummer - Abteilung für Neuroradiologie, Carl Gustav Carus Universitätsklinikum der Technischen Universität Dresden

Deutsche Gesellschaft für Neurochirurgie. 58. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie e.V. (DGNC). Leipzig, 26.-29.04.2007. Düsseldorf: German Medical Science GMS Publishing House; 2007. DocSA.05.03

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/dgnc2007/07dgnc159.shtml

Veröffentlicht: 11. April 2007

© 2007 Schackert et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

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Objective: The goal of modern neurosurgical technics is the implementation of functional data into neurosurgical procedures. Therefore, the planning of surgical approaches has to combine morphological and functional data. DTI provides a reliable tool to visualize fiber tracts that can be integrated by image fusion with MRI in neuronavigational systems. This is especially helpful in glioma surgery, where the definition of clear tumor margins is difficult. The development of a three-dimensional representation of white matter fiber tracts may improve our knowledge and the reliability of fiber tracking.

Methods: Fourty patients with low and high grade gliomas in the vicinity of the pyramidal tract were analyzed with respect to dislocation, edema, infiltration and disruption. Preoperative visual analysis and mean fractional anisotropy have been evaluated with respect to neurological deficits. The intraoperative verification of the pyramidal tract was approached by intraoperative stimulation of the fiber tract. In addition, a new three-dimensional visualization technique of the white matter has been developed by use of diffusion tensor patterns, matching the fiber texture of the brain. The new technique allows six degrees of freedom to visualize tensor parameters compared to common techniques with only two degrees of freedom. This approach is expected to provide information about the reliability of the DT datasets which are important for the data interpretation.

Results: The evaluation of fourty patients demonstrates that a correlation to the clinical symptoms can be detected only in patients with a disruption of the pyramidal tract. This averages about 50%. Infiltration of the fibers by tumor cells cannot predict clinical symptoms. Intraoperative stimulation of the pyramidal tract is diffult to interpret in a neuronavigational system. Brain shift and edema cause unsolved problems. The three-dimensional diffusion tensor pattern is a promising technique for visualizing the brain fiber texture in explorative 3D datasets providing a more complete perception of the tumor adjacent tissue for the neurosurgeon.

Conclusions: Functional data, e.g., DTI provide an important tool for the planning of surgical approaches in brain tumors. The reliability of fiber tracking can be improved by three-dimensional visualization of the fibers and statistic approaches in future applications. Diffusion tensor patterns may be an appropriate method for gaining data that resemble the real morphological situation.