Semi-Automated Soft-Tissue Acquisition and Modeling for Surgical Simulation

Realistic surgical simulation should take into account the mechanical properties of soft tissue, and also patient-specific variations. Consequently automated methods are required for the acquisition and modeling of patient-specific tissue properties. We take a step toward this goal by presenting a semi-automated method for acquiring, modeling, and simulating soft-tissue deformation. During a typical surgical procedure, organs are subject to tension, compression, and shear, so these three deformation modes must be represented in any soft tissue model. We measure these modes by performing ex vivo tests on porcine liver tissue and use the results to estimate material constants of a previously proposed combined logarithmic Ogden model. This model is capable of representing the non-linear stress-strain relations observed in the tissue, but these same non-linearities also introduce simulation challenges. In particular, eigenvalue degeneracies complicate automatic code generation, and element inversion can produce poorly conditioned matrices. We describe a method for avoiding these degeneracies, and present an automatic method of tuning the parameters of an existing element inversion scheme. Finally, computer animations of surgical simulation reveal a qualitative improvement in the non-linear behavior of soft tissues when compared to simulations using traditional material models.