An Approach for Intersubject Analysis of 3D Brain Images Based on Conformal Geometry

Abstract

Recent advances in imaging technologies, such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) have accelerated brain research in many aspects. In order to better understand the synergy of the many processes involved in normal brain function, integrated modeling and analysis of MRI, PET, and DTI across subjects is highly desirable. The current state-of-art computational tools fall short in offering an analytic approach for intersubject brain registration and analysis. In this paper we present an approach which is based on landmark constrained conformal parameterization of a brain surface from high-resolution structural MRI data to a canonical spherical domain. This model allows natural integration of information from co-registered PET as well as DTI data and lays a foundation for the quantitative analysis of the relationship among diverse datasets across subjects. Consequently, the approach can be extended to provide a software environment able to facilitate detection of abnormal functional brain patterns in patients with neurological disorder.

Evaluation of fiber bundles across subjects through brain mapping and registration of diffusion tensor data

“…These functions completely define the surface S 2 uniquely except for a rigid rotation . A Cartesian coordinate system is defined which specifies a 3D rotation transformation to guarantee a unique orientation of the sphere (Zou et al, 2006). Note that this mapping is only a partial step for creating the template CBM.…”

“…A landmark-constrained conformal mapping (Zou et al, 2006) is described below. Given a set of point's {P i } representing the landmarks, conformal mapping is employed on a template brain without any landmark constraints.…”

Multimodal medical imaging repository and information system in epilepsy surgery

“…It is now possible to delineate structural and functional information of the part of the brain that causes seizures in patients with epilepsy disorder by combining EEG, MRI, and fMRI findings [11,19,20]. They can help to better localize the focus [21][22][23][24].…”

Geodesic Distance-weighted Shape Vector Image Diffusion

“…For example, if the parameterization is conformal, the surface can be uniquely determined by the mean curvatures with area stretching factors defined on the parametric domain. In [7], genus zero surface conformal mapping was discussed and it was adopted for the brain surface mapping [31]. The conformal parameterization can be uniquely determined by two corresponding points.…”