Integrated parcellation and normalization using DTI fasciculography

  • Authors:

  • Hon Pong Ho;Fei Wang;Xenophon Papademetris;Hilary P. Blumberg;Lawrence H. Staib

  • Affiliations:

  • Departments of Biomedical Engineering, Yale University, New Haven, CT;Departments of Psychiatry, Yale University, New Haven, CT;Departments of Biomedical Engineering, Yale University, New Haven, CT and Departments of Diagnostic Radiology, Yale University, New Haven, CT;Departments of Diagnostic Radiology, Yale University, New Haven, CT and Departments of Psychiatry, Yale University, New Haven, CT;Departments of Biomedical Engineering, Yale University, New Haven, CT and Departments of Electrical Engineering, Yale University, New Haven, CT and Departments of Diagnostic Radiology, Yale Univer ...

  • Venue:
  • MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
  • Year:
  • 2011

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Abstract

Existing methods for fiber tracking, interactive bundling and editing from Diffusion Magnetic Resonance Images (DMRI) reconstruct white matter fascicles using groups of virtual pathways. Classical numerical fibers suffer from image noise and cumulative tracking errors. 3D visualization of bundles of fibers reveals structural connectivity of the brain; however, extensive human intervention, tracking variations and errors in fiber sampling make quantitative fascicle comparison difficult. To simplify the process and offer standardized white matter samples for analysis, we propose a new integrated fascicle parcellation and normalization method that combines a generic parametrized volumetric tract model with orientation information from diffusion images. The new technique offers a tract-derived spatial parameter for each voxel within the model. Cross-subject statistics of tract data can be compared easily based on these parameters. Our implementation demonstrated interactive speed and is available to the public in a packaged application.