Stiffness gradient detection for tissue discrimination based on identification of mass-spring-damper models

Discrimination of tumorous and healthy tissue is a critical task in minimally invasive surgery because mechanic palpation techniques used in open surgery cannot be applied. The surgeons rely on alternative sensor data such as images (camera) or displacements (force sensor). In order to take advantage of these sensor data beyond the pure visual inspection a detailed soft tissue model as well as an reliable parameter identification algorithm are required. In this contribution, a tissue model based on mass-spring-damper elements is selected due to its excellent approximation performance. As this model is high dimensional, a parametric model order reduction technique is applied in order to obtain a model which can be used for real-time parameter identification. It is shown that the parameter identification algorithm using the reduced order model offers accurate results, even in the presence of measurement noise.