3D brain mapping using a deformable neuroanatomy

TLDR

The study introduces two mathematical methods, used alone or together, to model shape variability in normal human neuroanatomies. The methods employ a digitized textbook of a typical neuroanatomy, applying probabilistic transformations constrained by elastic solid physics in one approach and viscous fluid physics in the other to match other normal neuroanatomies. Results show that a single deformable textbook can successfully model normal shape variability.

Abstract

Presents two different mathematical methods that can be used separately or in conjunction to accommodate shape variabilities between normal human neuroanatomies. Both methods use a digitized textbook to represent the complex structure of a typical normal neuroanatomy. Probabilistic transformations on the textbook coordinate system are defined to accommodate shape differences between the textbook and images of other normal neuroanatomies. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second. Results presented in this paper demonstrate how a single deformable textbook can be used to accommodate normal shape variability.