Diffeomorphometry, geodesic positioning systems for human anatomy

"This is a remarkable combination of algorithm development and software technology that provides the basis for extending classical notions used for the positioning of rigid bodies adapted for the positioning of deformable bodies appropriate to human coordinates systems. Con-necting this to modern machine learning algorithms opens the door for future research and clinical applications in which high throughput massive databases of structural and functional anatomy can be indexed and searched, analogous to the current state of the art in GoogleMaps," says Mi-chael I. Miller Ph.D., a professor of Biomedical Engineering of the Johns Hopkins University and senior author on this paper.

By linking notions from Lagrangian and Hamiltonian mechanics of rigid bodies, the investigators have defined human shape as a Riemannian metric space generalizing D'Arcy Thompson's classical formulation of mathematical morphology of shape and form, with the metric structure defined by the geodesic flow of coordinates connecting one shape to another.

"Once human shape is embedded in these Riemannian or geodesic coordinates, then human anatomy can be indexed and searched," Miller continued. "This is a beautiful example of how advances in imaging technology coupled to computational and algorithmic methods are enabling both biological discovery and clinical applications."

Using large volumes of spatial and temporal data being generated via high throughput imaging systems, the investigators in the United States and Europe are exploiting these geodesic positioning systems to uncover new biomarkers and diagnostic parameters that may provide clues to fundamental disease mechanisms in several neuropsychiatric illnesses, including dementia, Huntingdon's, schizophrenia, autism and mood disorder diseases.