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Robust Estimation of Group-wise Cortical Correspondence with an Application to Macaque and Human Neuroimaging Studies

Institution:
1Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
2Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
3Department of Biomedical Engineering, Korea University, Seoul, South Korea.
4R&D Team, Health and Medical Equipment Business, Samsung Electronics, Suwon, South Korea.
5Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
6Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Emory university, Atlanta, GA, USA.
Publication Date:
Jun-2015
Volume Number:
9
Pages:
210
Citation:
Front Neurosci. 2015 Jun; 9: 210.
PubMed ID:
26113807
PMCID:
PMC4462677
Keywords:
group-wise registration, cortical surface, spherical harmonics, entropy minimization, sulcal curve, surface registration
ISBN:
Appears in Collections:
NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
P50 MH078105/MH/NIMH NIH HHS/United States
P50 MH100029/MH/NIMH NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
U54 HD079124/HD/NICHD NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
Generated Citation:
Lyu I., Kim S.H., Seong J-K., Yoo S.W., Evans A., Shi Y., Sanchez M., Niethammer M., Styner M.A. Robust Estimation of Group-wise Cortical Correspondence with an Application to Macaque and Human Neuroimaging Studies. Front Neurosci. 2015 Jun; 9: 210. PMID: 26113807. PMCID: PMC4462677.
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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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