Probabilistic inverse problem to characterize tissue-equivalent material mechanical properties

The understanding of internal processes that affect the changes of consistency of soft tissue is a challenging problem. An ultrasound-monitoring Petri dish has been designed to monitor the evolution of relevant mechanical parameters during engineered tissue formation processes in real time. A better understanding of the measured ultrasonic signals required the use of numerical models of the ultrasound-tissue interactions. The extraction of relevant data and its evolution with sufficient sensitivity and accuracy is addressed by applying well-known signal processing techniques to both the experimental and numerically predicted measurements. In addition, a stochastic model-class selection formulation is used to rank which of the proposed interaction models are more plausible. The sensitivity of the system is verified by monitoring a gelation process.