Enhanced cortical thickness measurements for rodent brains via Lagrangian-based RK4 streamline computation

Joohwi Lee; Sun Hyung Kim; Ipek Oguz; Martin Styner

doi:10.1117/12.2216420

21 March 2016 Enhanced cortical thickness measurements for rodent brains via Lagrangian-based RK4 streamline computation

Joohwi Lee, Sun Hyung Kim, Ipek Oguz, Martin Styner

Author Affiliations +

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97840B (2016) https://doi.org/10.1117/12.2216420
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

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Joohwi Lee, Sun Hyung Kim, Ipek Oguz, and Martin Styner "Enhanced cortical thickness measurements for rodent brains via Lagrangian-based RK4 streamline computation", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840B (21 March 2016); https://doi.org/10.1117/12.2216420

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