TY - JOUR
T1 - Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis
AU - Corouge, Isabelle
AU - Fletcher, P. Thomas
AU - Joshi, Sarang
AU - Gouttard, Sylvain
AU - Gerig, Guido
N1 - Funding Information:
This research is supported by the NIH NIBIB Grant P01 EB002779, the NIMH Silvio Conte Center for Neuroscience of Mental Disorders MH064065, and the UNC Neurodevelopmental Disorders Research Center HD 03110. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics . We acknowledge the Insight Toolkit community for providing the software framework for the DTI analysis algorithms. Dr. Weili Lin, UNC Radiology, and James Mc Fall, Duke University, are acknowledged for providing us with the DT MRI data.
PY - 2006/10
Y1 - 2006/10
N2 - Quantitative diffusion tensor imaging (DTI) has become the major imaging 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 of 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 systematically includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. A new measure of tensor anisotropy, called geodesic anisotropy (GA) is applied and compared with FA. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics (average and variance) calculated within cross-sections. Feasibility of our approach is demonstrated on various fiber tracts of a single data set. A validation study, based on six repeated scans of the same subject, assesses the reproducibility of this new DTI data analysis framework.
AB - Quantitative diffusion tensor imaging (DTI) has become the major imaging 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 of 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 systematically includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. A new measure of tensor anisotropy, called geodesic anisotropy (GA) is applied and compared with FA. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics (average and variance) calculated within cross-sections. Feasibility of our approach is demonstrated on various fiber tracts of a single data set. A validation study, based on six repeated scans of the same subject, assesses the reproducibility of this new DTI data analysis framework.
KW - DTI analysis
KW - Diffusion tensor interpolation
KW - Diffusion tensor statistics
KW - Fiber tract modeling
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U2 - 10.1016/j.media.2006.07.003
DO - 10.1016/j.media.2006.07.003
M3 - Article
C2 - 16926104
AN - SCOPUS:33747830497
SN - 1361-8415
VL - 10
SP - 786
EP - 798
JO - Medical Image Analysis
JF - Medical Image Analysis
IS - 5
ER -