Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace –Beltrami embedding space

Surface mapping methods play an important role in various scientific discoveries from tracking the maturation of adolescent brains (Gogtay  et al., 2004), mapping gray matter atrophy patterns in Alzheimer’s disease (AD) (Thompson et al., 2004), to studying early prenatal and postnatal brain development (Li et al., 2013; Nie et al., 2010). The backbone of surface mapping methods is the computational technique that establishes de tailed one-to-one correspondences across different brain surfaces. In this work, we propose a novel computational framework for establishing surface correspondences in the Laplace–Beltrami embedding space (Rustamov, 2007).

Source: Medical Image Analysis - March 16, 2018 Category: Radiology Authors: Jin Kyu Gahm, Yonggang Shi, for the Alzheimer ’s Disease Neuroimaging Initiative Source Type: research