Application of multi-resolution modality independent elastography for detection of multiple anomalous objects

This work extends a recently realized inverse problem technique of extracting soft tissue elasticity information via nonrigid 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, nonlinear 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.