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Proceedings Paper

Evaluation of topology correction methods for the generation of the cortical surface
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Paper Abstract

The cerebral cortex is a highly convoluted anatomical structure. The folding pattern defined by sulci and gyri is a complex pattern that is very heterogeneous across subjects. The heterogeneity across subjects has made the automated labeling of this structure into its constituent components a challenge to the field of neuroimaging. One way to approach this problem is to conformally map the surface to another representation such as a plane or sphere. Conformal mapping of the surface requires that surface to be topologically correct. However, noise and partial volume artifacts in the MR images frequently cause holes or handles to exist in the surface that must be removed. Topology correction techniques have been proposed that operate on the cortical surface, the original image data, and hybrid methods have been proposed. This paper presents an experimental assessment of two different topology correction methods. The first approach is based on modification of 3D voxel data. The second method is a hybrid approach that determines the location of defects from the surface representation while repairing the surface by modifying the underlying image data. These methods have been applied to 10 brains, and a comparison is made among them. In addition, detailed statistics are given based on the voxel correction method. Based on these 10 MRI datasets, we have found the hybrid method incapable of correcting the cortical surface appropriately when a handles and holes exist in close proximity. In several cases, holes in the anatomical surface were labeled as handles thus resulting in discontinuities in the folding pattern. The image-based approach in this study was found to correct the topology in all ten cases within a reasonable time. Furthermore, the distance between the original and corrected surfaces, thickness of brain cortex, curvatures and surface areas are provided as assessments of the approach based on our datasets.

Paper Details

Date Published: 13 March 2009
PDF: 8 pages
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 726132 (13 March 2009); doi: 10.1117/12.812323
Show Author Affiliations
Wen Li, The Univ. of Iowa (United States)
Vincent A. Magnotta, The Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 7261:
Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kenneth H. Wong, Editor(s)

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