In addition to the 2-D strain map, which can be recovered from the displacement field, other elasticity parameters can be obtained only at the interface between indenter and specimen. Knowledge of the indenter geometry, the force applied, and the displacement obtained at the indenter position enables the recovery of elastic parameters such as stress and shear modulus for only this point. However, the goal is to retrieve the elastic parameters for all imaged object points and to show their corresponding 3-D distribution. The 3-D information is essential in order to locate the tumor in minimally invasive surgery and hence to support the navigation process. Hence, a solution to an under-defined system needs to be found in order to recover the spatial distribution of elastic parameters. An inverse problem has to be solved, which can be accomplished via solving a direct problem. The underlying physical laws applicable to both, inverse and direct problem, are the same, but inverted, and relate the elastic properties to a measurable mechanical response. The direct problem is well defined with respect to the geometrical (dimensions of tissue and foreign body, indenter geometry, and force) and elastic input parameters (elastic modulus for linear elastic, shear modulus, and locking stretch for nonlinear elastic). It starts with the desired elastic parameters as input parameters, whereas the experimentally obtained parameters (displacement field and strain map) represent the output, which can be compared with the measured values, see Fig. 2. Hence, it is an inverted process of the inverse problem and is therefore referred to as a forward problem.21 In that manner, the desired unknown elastic parameters, which experimentally cannot be retrieved, can be obtained from a realistically well-matched FE model that enables generating the computational solution of the direct problem. Solving the direct problem can be implemented using a noniterative and iterative approach. The noniterative approach requires a good estimate of the input parameters that can be generated from a priori knowledge based on stress–strain measurements about the different materials/tissues and the geometry of the indenter and object under investigation. The generation of relevant data to apply the noniterative method for medical applications can be obtained from previous operations on patients of similar conditions (age, gender, and life style) or preoperational data. Moreover, the geometry/morphology of inner organs such as kidney and the locations of abnormalities (cancer) can be obtained from preoperational data such as CT and MRT data.