Sonia Pujol1, William Wells1, Carlo Pierpaoli2, Caroline Brun3, James Gee3, Guang Cheng4, Baba Vemuri4, Olivier Commowick5, Sylvain Prima5, Aymeric Stamm5, Maged Goubran6, Ali Khan6, Terry Peters6, Peter Neher7, Klaus H Maier-Hein7, Yundi Shi8, Antonio Tristan-Vega9, Gopalkrishna Veni10, Ross Whitaker10, Martin Styner8, Carl-Fredrik Westin11, Sylvain Gouttard10, Isaiah Norton12, Laurent Chauvin13, Hatsuho Mamata1, Guido Gerig10, Arya Nabavi14, Alexandra Golby12, Ron Kikinis1. 1. Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. 2. Program on Pediatric Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda. 3. Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia. 4. Department of Computer and Information Science and Engineering, University of Florida, Gainesville. 5. University of Rennes I, VISAGES INSERM - U746 CNRS UMR6074 - INRIA, Rennes, France. 6. Imaging Laboratories, Robarts Research Institute, Western University, London, ON, Canada. 7. Junior Group Medical Image Computing, Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany. 8. Department of Psychiatry and Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC. 9. Department of Mechanical Engineering, Universidad de Valladolid, Valladolid, Spain. 10. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT. 11. Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. 12. Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. 13. Surgical Navigation and Robotics Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. 14. International Neuroscience Institute (INI), Hannover, Germany.
Abstract
BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop. METHODS: Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods. RESULTS: The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues. CONCLUSIONS: The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.
BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop. METHODS: Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods. RESULTS: The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues. CONCLUSIONS: The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.
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