Recovery of soft tissue object deformation from 3D image sequences using biomechanical models

Abstract

The estimation of soft tissue deformation from 3D image sequences is an important problem in a number of fields such as diagnosis of heart disease and image guided surgery. In this paper we describe a methodology for using biomechanical material models, within a Bayesian framework which allows for proper modeling of image noise, in order to estimate these deformations. The resulting partial differential equations are discretized and solved using the finite element method. We demonstrate the application of this method to estimating strains from sequences of in-vivo left ventricular MR images, where we incorporate information about the fibrous structure of the ventricle. The deformation estimates obtained exhibit similar patterns with measurements obtained from more invasive techniques, used as a gold standard.