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Shape-based normalization of the corpus callosum for DTI connectivity analysis.

Sun, H; Yushkevich, PA; Zhang, H; Cook, PA; Duda, JT; Simon, TJ; Gee, JC; (2007) Shape-based normalization of the corpus callosum for DTI connectivity analysis. IEEE Trans Med Imaging , 26 (9) pp. 1166-1178. 10.1109/TMI.2007.900322.

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The continuous medial representation (cm-rep) is an approach that makes it possible to model, normalize, and analyze anatomical structures on the basis of medial geometry. Having recently presented a partial differential equation (PDE)-based approach for 3-D cm-rep modeling [1], here we present an equivalent 2-D approach that involves solving an ordinary differential equation. This paper derives a closed form solution of this equation and shows how Pythagorean hodograph curves can be used to express the solution as a piecewise polynomial function, allowing efficient and robust medial modeling. The utility of the approach in medical image analysis is demonstrated by applying it to the problem of shape-based normalization of the midsagittal section of the corpus callosum. Using diffusion tensor tractography, we show that shape-based normalization aligns subregions of the corpus callosum, defined by connectivity, more accurately than normalization based on volumetric registration. Furthermore, shape-based normalization helps increase the statistical power of group analysis in an experiment where features derived from diffusion tensor tractography are compared between two cohorts. These results suggest that cm-rep is an appropriate tool for normalizing the corpus callosum in white matter studies.

Type: Article
Title: Shape-based normalization of the corpus callosum for DTI connectivity analysis.
Location: United States
DOI: 10.1109/TMI.2007.900322
Keywords: Agenesis of Corpus Callosum, Algorithms, Artificial Intelligence, Child, Computer Simulation, Corpus Callosum, Diffusion Magnetic Resonance Imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Models, Neurological, Models, Statistical, Nerve Fibers, Myelinated, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique
URI: http://discovery.ucl.ac.uk/id/eprint/91124
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