A Modality Independent Approach to Elasticity Imaging

Abstract

The correlation between the stiffness and health of tissue is an accepted form of organ disease assessment. As a result, there has been a significant amount of interest in developing methods to image elasticity parameters (i.e. elastography). This work presents a technique that frames the elastography imaging problem within a non-rigid iterative registration approach. Through the use of finite element modeling and image comparison methods, material properties are varied in order to optimize the registration between a post-compressed image and a model-generated compressed image. The results shown here demonstrate the strong connection between image similarity and appropriate tissue parameters and the algorithm's ability to detect contrast in tissue stiffness. Simulations demonstrate that the method is effective over a wide range of scenarios. Also, we were successfully able to localize regions of stiffness within phantom data taken in both CT and MRI. By casting elasticity image reconstruction within the context of image similarity, the method is generalized to all forms of medical imaging.