Data assimilation using particle filter for real-time identification of organ properties

The number of operations using surgical robots are continuously increasing. To perform accurate surgeries, it is necessary to know the behaviour of intervened organs, especially their mechanical properties, which must be accurately determined. However, the physical properties of organs vary depending on age, gender, and environment, and thus, each organ exhibits particular mechanical properties. The authors propose a real-time assimilation system that identifies organ properties. Specifically, a 2D model using the finite element method and data assimilation, which is mostly used in Earth science, allows the identification of the physical parameters of organs. Data assimilation relies on a particle filter for efficiently solving the non-linear identification of parameters from a statics viewpoint. In addition, the semi-implicit Euler method discretises the proposed model and improves efficiency. The proposed approach can serve to the future implementation of a real-time and accurate framework for identifying mechanical properties of organs.