This work explores an 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 framework capable of handling fully three-dimensional models and image data has been recently developed utilizing parallel computing and iterative sparse matrix solvers. For this preliminary investigation, a series of simulation experiments with clinical image data of human breast are used to test the robustness of the algorithm to expected mis-estimation of displacement boundary conditions encountered in real-world situations. Three methods of automated point correspondence are also examined as means of generating boundary conditions for the algorithm.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.