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Proceedings Paper

Enhanced cortical thickness measurements for rodent brains via Lagrangian-based RK4 streamline computation
Author(s): Joohwi Lee; Sun Hyung Kim; Ipek Oguz; Martin Styner
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Paper Abstract

The cortical thickness of the mammalian brain is an important morphological characteristic that can be used to investigate and observe the brain’s developmental changes that might be caused by biologically toxic substances such as ethanol or cocaine. Although various cortical thickness analysis methods have been proposed that are applicable for human brain and have developed into well-validated open-source software packages, cortical thickness analysis methods for rodent brains have not yet become as robust and accurate as those designed for human brains. Based on a previously proposed cortical thickness measurement pipeline for rodent brain analysis,1 we present an enhanced cortical thickness pipeline in terms of accuracy and anatomical consistency. First, we propose a Lagrangian-based computational approach in the thickness measurement step in order to minimize local truncation error using the fourth-order Runge-Kutta method. Second, by constructing a line object for each streamline of the thickness measurement, we can visualize the way the thickness is measured and achieve sub-voxel accuracy by performing geometric post-processing. Last, with emphasis on the importance of an anatomically consistent partial differential equation (PDE) boundary map, we propose an automatic PDE boundary map generation algorithm that is specific to rodent brain anatomy, which does not require manual labeling. The results show that the proposed cortical thickness pipeline can produce statistically significant regions that are not observed in the previous cortical thickness analysis pipeline.

Paper Details

Date Published: 21 March 2016
PDF: 10 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840B (21 March 2016); doi: 10.1117/12.2216420
Show Author Affiliations
Joohwi Lee, The Univ. of North Carolina at Chapel Hill (United States)
Sun Hyung Kim, The Univ. of North Carolina at Chapel Hill (United States)
Ipek Oguz, The Univ. of Iowa (United States)
Martin Styner, The Univ. of North Carolina at Chapel Hill (United States)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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