TY - GEN
T1 - Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis
AU - Corouge, Isabelle
AU - Fletcher, P. Thomas
AU - Joshi, Sarang
AU - Gilmore, John H.
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 - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - DTI analysis
KW - Diffusion tensor interpolation
KW - Diffusion tensor statistics
KW - Fiber tract modeling
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U2 - 10.1007/11566465_17
DO - 10.1007/11566465_17
M3 - Conference contribution
C2 - 16685838
AN - SCOPUS:33744822209
SN - 3540293272
SN - 9783540293279
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 131
EP - 139
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings
T2 - 8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005
Y2 - 26 October 2005 through 29 October 2005
ER -