Surface fluid registration of conformal representation: Application to detect disease burden and genetic influence on hippocampus

• Published in: NeuroImage (Orlando, Fla.) Vol. 78; pp. 111 - 134
• Main Authors: Shi, Jie, Thompson, Paul M., Gutman, Boris, Wang, Yalin
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.09.2013; Elsevier; Elsevier Limited
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - genetics; Alzheimer Disease - pathology; Apolipoprotein E4 - genetics; Automation; Biological and medical sciences; Brain Mapping - methods; Brain research; Cognitive Dysfunction - genetics; Cognitive Dysfunction - pathology; Conformal representation; Databases, Factual; Diffusion Tensor Imaging; Female; Fundamental and applied biological sciences. Psychology; Genetic Predisposition to Disease - genetics; Genotype; Hippocampus - pathology; Humans; Image Interpretation, Computer-Assisted - methods; Male; Methods; Middle Aged; Neural networks; Nonlinear image registration; Presymptomatic AD; Surface conformal parameterization; Surface fluid registration; Tensor-based morphometry; Vertebrates: nervous system and sense organs; Nonlinear image registration; Surface conformal parameterization; Conformal representation; Surface fluid registration; Tensor-based morphometry; Presymptomatic AD; Central nervous system; Morphometry; Hippocampus; Encephalon
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.04.018

In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E∈4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research. •Efficient formulation of manifold fluid registration by conformal parameterization•An inverse consistent non-linear surface registration scheme•Surface multivariate TBM was adopted as morphometry statistics in the system.•The system achieved better performance than some other surface registration tools.•ApoE4 may be associated with accelerated brain atrophy on MCI patients and controls.