Boundary estimation of fiber bundles derived from diffusion tensor images

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• 作者：Miriam Helen Anna Bauer (1)
  Sebastiano Barbieri (2)
  Jan Klein (2)
  Jan Egger (1)
  Daniela Kuhnt (1)
  Bernd Freisleben (3)
  Horst Karl Hahn (2)
  Christopher Nimsky (1)
  
• 关键词：Diffusion tensor imaging ; Fiber tracking ; Boundary estimation ; Ray ; based approach
• 刊名：International Journal of Computer Assisted Radiology and Surgery
• 出版年：2011
• 出版时间：January 2011
• 年：2011
• 卷：6
• 期：1
• 页码：1-11
• 全文大小：979KB
• 参考文献：1. http://www.mevislab.de
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• 作者单位：Miriam Helen Anna Bauer (1)
  Sebastiano Barbieri (2)
  Jan Klein (2)
  Jan Egger (1)
  Daniela Kuhnt (1)
  Bernd Freisleben (3)
  Horst Karl Hahn (2)
  Christopher Nimsky (1)
  
  1. Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35033, Marburg, Germany
  2. Fraunhofer MEVIS, Institute for Medical Image Computing, Universitaetsallee 29, 28359, Bremen, Germany
  3. Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Strasse 3, 35032, Marburg, Germany
  

文摘

Purpose Diffusion tensor imaging (DTI) is a non-invasive imaging technique that allows estimating the location of white matter tracts based on the measurement of water diffusion properties. Using DTI data, the fiber bundle boundary can be determined to gain information about eloquent structures, which is of major interest for neurosurgical interventions. In this paper, a novel approach for boundary estimation is presented. Methods DTI in combination with diverse segmentation algorithms allows estimating the position and course of fiber tracts in the human brain. For additional information about the expansion of the fiber bundle, the introduced iterative approach uses the centerline of a tracked fiber bundle between two regions of interest (ROI). After sampling along this centerline, rays are sent out radially, discrete 2D contours are calculated, and the fiber bundle boundary is estimated in a stepwise manner. For this purpose, each ray is analyzed using several criteria, including anisotropy parameters and angle parameters, to find the boundary point. Results The novel method for automatically calculating the boundaries has been applied to several artificially generated DTI datasets. Multiple parameters were varied: number of rays per plane, sampling rate and sampled points along the rays. For the DTI data used in the experiments, the method yielded a dice similarity coefficient (DSC) between 74.7 and 91.5%. Conclusions In this paper, a novel approach to retrieve significant information about the fiber bundle boundary from DTI data is presented. The method is a contribution to gather important knowledge about high-risk structures in neurosurgical interventions.