Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis

Authors:
Isabelle Corouge;P. Thomas Fletcher;Sarang Joshi;John H. Gilmore;Guido Gerig
Affiliations:
Departments of Computer Science, University of North Carolina, Chapel Hill;Scientific Computing and Imaging Institute, University of Utah;Departments of Computer Science, University of North Carolina, Chapel Hill;Departments of Psychiatry, University of North Carolina, Chapel Hill;Departments of Computer Science, University of North Carolina, Chapel Hill
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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Abstract

Diffusion tensor imaging (DTI) has become the major modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics based on tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics calculated within cross-sections. Examples from a clinical neuroimaging study of the early developing brain illustrate the potential of this new method to assess white matter fiber maturation and integrity.