gms | German Medical Science

57th Annual Meeting of the German Society of Neurosurgery
Joint Meeting with the Japanese Neurosurgical Society

German Society of Neurosurgery (DGNC)

11 - 14 May, Essen

Diffusion tensor imaging: visualization of brain tumours using the method of tensor patterns

Diffusions-Tensor-Bildgebung: Visualisierung von Hirntumoren mittels der Tensor-Pattern-Methode

Meeting Abstract

  • corresponding author H. Kitzler - University Hospital Dresden, Dept. of Neuroradiology, Dresden
  • W. Benger - Zuse Institute Berlin (ZIB), Berlin-Dahlem
  • A. Werner - University Hospital Dresden, Dept. of Neuroradiology, Dresden
  • H. Bartsch - Mercury Systems, San Diego, California, USA
  • A. Shumilina - Technical University of Berlin
  • H.-C. Hege - Zuse Institute Berlin (ZIB), Berlin-Dahlem
  • G. Schackert - University Hospital Dresden, Dept. of Neurosurgery, Dresden
  • R. von Kummer - University Hospital Dresden, Dept. of Neuroradiology, Dresden

Deutsche Gesellschaft für Neurochirurgie. Japanische Gesellschaft für Neurochirurgie. 57. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie e.V. (DGNC), Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie. Essen, 11.-14.05.2006. Düsseldorf, Köln: German Medical Science; 2006. DocP 04.46

The electronic version of this article is the complete one and can be found online at:

Published: May 8, 2006

© 2006 Kitzler et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Objective: Diffusion Tensor Imaging (DTI) has the potential to depict the relation of brain white matter structures and gliomas. For neurosurgical purposes visualization is arrogated that allows comprehending the relevant information of imminent danger to eloquent structures. So far visualization of the complete tensor information is uncommon, because of limited availability of appropriate visualization methods.

Methods: We utilized the method of tensor patterns1. Measurements were performed on a 1.5 T scanner (Sonata, Siemens, Germany). An optimized diffusion weighted EPI sequence (TR/TE: 4000/129ms; FOV: 230x230mm; slice: 2mm; matrix: 1282; EPI-factor: 128; b-value 750, 6 directions) was applied on four patients featuring low grade gliomas (Astrocytoma WHO II, one gemistocytic tumour with focal transformation towards WHO III). An additional 3D-FLAIR sequence (TR/TE: 6000/354 ms; FOV: 232x256mm; slice: 1mm; 160 slices; matrix: 206x256) was obtained. Image data processing was performed using a superset of Amira 3.12.

Results: Initial application demonstrated that the method of tensor patterns serves neurosurgical requirements of an integral 3D presentation. It is sensitive to small variations in the tensor field. Isotropy and high diffusion velocity were observed in tumours. It reveals DTI changes by far exceeding the pathologic T2 signal changes. While colouring provides a rough overview to distinguish among regions with highly linear and planar diffusion, detailed texture shows the orientation of the dominant diffusion.

Conclusions: DTI post-processing methods extract certain features of DTI data sets. The common employment of stream-tubes to visualize linear structures and stream surfaces to visualize planar structures results in an abrupt visual change between the two structures. This aggravates the measuring error of the tensor field. The method of tensor patterns provides a smooth transition between tensor properties and the ability to detect minor changes of the tensor information. Thus it is more appropriate to visualize pathologic changes of white matter integrity on base of DTI data.

1) Visualization of Brain Neuronal Structures for Tumor Detection via Diffusion Tensor MRI. Benger W, Bartsch H, Choumilina A, Hege HC, Kitzler H, Werner A. International Journal of Neuroscience, 2006, in press

2) Amira: A Highly Interactive System for Visual Data Analysis. Stalling D, Westerhoff M, Hege HC in: Hansen CD, Johnson CR (eds), The Visualization Handbook, Chapter 38, pp. 749-767, Elsevier, 2005