Research Papers: Imaging

T1 magnetic resonance imaging head segmentation for diffuse optical tomography and electroencephalography

[+] Author Affiliations
Katherine L. Perdue

Dartmouth College, Thayer School of Engineering, 14 Engineering Drive, Hanover, New Hampshire 03755

Solomon G. Diamond

Dartmouth College, Thayer School of Engineering, 14 Engineering Drive, Hanover, New Hampshire 03755

J. Biomed. Opt. 19(2), 026011 (Feb 14, 2014). doi:10.1117/1.JBO.19.2.026011
History: Received November 4, 2013; Revised January 5, 2014; Accepted January 16, 2014
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Abstract.  Accurate segmentation of structural magnetic resonance images is critical for creating subject-specific forward models for functional neuroimaging source localization. In this work, we present an innovative segmentation algorithm that generates accurate head tissue layer thicknesses that are needed for diffuse optical tomography (DOT) data analysis. The presented algorithm is compared against other publicly available head segmentation methods. The proposed algorithm has a root mean square scalp thickness error of 1.60 mm, skull thickness error of 1.96 mm, and summed scalp and skull error of 1.49 mm. We also introduce a segmentation evaluation metric that evaluates the accuracy of tissue layer thicknesses in regions of the head where optodes are typically placed. The presented segmentation algorithm and evaluation metric are tools for improving the localization accuracy of neuroimaging with DOT, and also multimodal neuroimaging such as combined electroencephalography and DOT.

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Citation

Katherine L. Perdue and Solomon G. Diamond
"T1 magnetic resonance imaging head segmentation for diffuse optical tomography and electroencephalography", J. Biomed. Opt. 19(2), 026011 (Feb 14, 2014). ; http://dx.doi.org/10.1117/1.JBO.19.2.026011


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