MS-13 Inverse Modelling and Uncertainty Quantification in Biomechanics

Ankush Aggarwal, University of Glasgow
Andrew McBride, University of Glasgow
John C Brigham, Durham University

With the development of detailed biomechanical models, estimating the associated parameters has become a major challenge. This is especially true with in-vivo datasets, where standard lab-based techniques are not feasible. Inverse models, wherein the parameters are calculated by solving an optimization problem, have become increasingly popular. These can be formulated in multiple ways. Moreover, the data available, especially from clinics, can have significant noise and, therefore, includes a significant uncertainty. Quantifying this uncertainty remains a challenge, especially given the high dimensionality of the datasets and complexity of the models.

This exciting mini-symposium will bring together researchers who focus on the development of inverse models and techniques to quantify uncertainty in tissue biomechanics, as well as their application towards specific problems. Research on new techniques for image registration and in-vivo strain calculation are also encouraged, together with statistical techniques for designing optimal experiments and selecting models.

Topics of Interest Include:

• Computational inverse model development for tissue biomechanics with application to in-vitro and in-vivo datasets
• Image-based characterization of tissue biomechanical properties
• Computational techniques for uncertainty quantification in biomechanics
• Application of statistical techniques for designing experiments and selecting models
• Image registration and processing techniques to derive tissue deformation and strain measures