Summary
We present a new model-based framework for the statistical analysis of diffusion imaging data associated with specific white matter tracts. The framework takes advantage of the fact that several of the major white matter tracts are thin sheet-like structures that can be effectively modeled by medial representations. The approach involves segmenting major tracts and fitting them with deformable geometric medial models. The medial representation makes it possible to average and combine tensor-based features along directions locally perpendicular to the tracts, thus reducing data dimensionality and accounting for errors in normalization. The framework enables the analysis of individual white matter structures, and provides a range of possibilities for computing statistics and visualizing differences between cohorts. The framework is demonstrated in a study of white matter differences in pediatric chromosome 22q11.2 deletion syndrome.
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Acknowledgments
This work was supported by the NIH grants AG027785 (PY), NS061111 (PY), HD042974 (TJC), HD046159 (TJC), MH068066 (V. Megalooikonomou), and NS045839 (J.A. Detre).
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Yushkevich, P.A., Zhang, H., Simon, T.J., Gee, J.C. (2009). Structure-Specific Statistical Mapping of White Matter Tracts. In: Laidlaw, D., Weickert, J. (eds) Visualization and Processing of Tensor Fields. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88378-4_5
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DOI: https://doi.org/10.1007/978-3-540-88378-4_5
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