Recovery of Soft Tissue Object Deformation from 3D Image Sequences Using Biomechanical Models

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 bridging biomechanical information regarding material properties with 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 invivo 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 Magnetic Resonance Tagging. An earlier version of this work will appear in [14].