This work extends a recently realized inverse problem technique of extracting soft tissue elasticity information via non-rigid model-based image registration. The algorithm uses the elastic properties of the tissue in a biomechanical model to achieve maximal similarity between image data acquired under different states of loading. A new multi-resolution, non-linear optimization framework has been employed which allows for improved performance and object detection. Prior studies have demonstrated successful reconstructions from images of a tissue-like thin membrane phantom with a single embedded inclusion that was significantly stiffer than its surroundings. For this investigation, a similar phantom was fabricated with two stiff inclusions to test the effectiveness of this method in discriminating multiple smaller objects. Elasticity values generated from both simulation and real data testing scenarios provided sufficient contrast for detection and good quantitative localization of the inclusion areas.
A significant amount of breast cancer research in recent years has been devoted to novel means of tumor detection such as MR contrast enhancement, electrical impedance tomography, microwave imaging, and elastography. Many of these detection methods involve deforming the breast. Often, these deformed images need to be correlated to anatomical images of the breast in a different configuration. In the case of our elastography framework, a series of comparisons between the pre- and post-deformed images needs to be performed. This paper presents an automatic method for determining correspondence between images of a pendant breast and a partially-constrained, compressed breast. The algorithm is an extension to the symmetric closest point approach of Papademetris et al. However, because of the unique deformation and shape change of a partially-constrained, compressed breast, the algorithm was modified through the use of iterative closest point (ICP) registration on easily identifiable sections of the breast images and through weighting the symmetric nearest neighbor correspondence. The algorithm presented in this paper significantly improves correspondence determination between the pre- and post-deformed images for a simulation when compared to the original Papademetris et al.'s symmetric closest point criteria.