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Robust estimation of group-wise cortical correspondence with an application to macaque and human neuroimaging studies

Authors
Lyu, IlwooKim, Sun H.Seong, Joon-KyungYoo, Sang W.Evans, AlanShi, YundiSanchez, MarNiethammer, MarcStyner, Martin A.
Issue Date
11-6월-2015
Publisher
FRONTIERS MEDIA SA
Keywords
group-wise registration; cortical surface; spherical harmonics; entropy minimization; sulcal curve; surface registration
Citation
FRONTIERS IN NEUROSCIENCE, v.9
Indexed
SCIE
SCOPUS
Journal Title
FRONTIERS IN NEUROSCIENCE
Volume
9
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/93270
DOI
10.3389/fnins.2015.00210
ISSN
1662-4548
Abstract
We present a novel group-wise registration method for cortical correspondence for local cortical thickness analysis in human and non-human primate neuroimaging studies. The proposed method is based on our earlier template based registration that estimates a continuous, smooth deformation field via sulcal curve-constrained registration employing spherical harmonic decomposition of the deformation field. This pairwise registration though results in a well-known template selection bias, which we aim to overcome here via a group-wise approach. We propose the use of an unbiased ensemble entropy minimization following the use of the pairwise registration as an initialization. An individual deformation field is then iteratively updated onto the unbiased average. For the optimization, we use metrics specific for cortical correspondence though all of these are straightforwardly extendable to the generic setting: The first focused on optimizing the correspondence of automatically extracted sulcal landmarks and the second on that of sulcal depth property maps. We further propose a robust entropy metric and a hierarchical optimization by employing spherical harmonic basis orthogonality. We also provide the detailed methodological description of both our earlier work and the proposed method with a set of experiments on a population of human and non-human primate subjects. In the experiment, we have shown that our method achieves superior results on consistency through quantitative and visual comparisons as compared to the existing methods.
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