HIERARCHICAL BRAIN MODEL FOR COREGISTRATION: A Physical Model for Analysis of Brain MRI Data

摘要

A ubiquitous problem in coregistration of brain images is that individual sulci and gyri vary considerably between individuals, both with respect to location and shape as well as for simple existence of particular sulci. The underlying assumption of most coregistration processes is that one structure can be smoothly morphed to exactly resemble another structure if enough parameters are used. Although in a strict sense this may be true for intersubject brain registration, due to differing structures the result may not be as meaningful as desired. The proposed approach offers a groundbreaking alternative to the standard approach of continuously deformable coregistration algorithms, introducing instead a hierarchical structure of related nodes (a "nodetree") to model the brain structure using grey-matter and white-matter masks. Additionally, a proposal is made for using the nodetree structure for coregistration, employing a novel locally discontinuous but focused registration to more accurately align and compare corresponding features. This approach can provide a framework for identifying structural differences, with a goal of relating them to functional differences. Although this method uses the brain as an example, it is quite general and not limited to the brain, or even to medical images.