Problem-specific optical systems for shape and deformation measurements in biomechanics
15 september 2016
UAntwerpen, Campus Groenenborger, Building U, Room U.025 - Groenenborgerlaan 171 - 2020 Antwerpen (route: UAntwerpen, Campus Groenenborger
Organisatie / co-organisatie:
Biophysics and BioMedical Physics
Lecture by Professor Katia Genovese, University of Basilicata, Italy
Recent advances in mechanobiology reveal more and more that many cell types, especially those responsible for establishing, maintaining, remodelling or repairing extracellular matrix, are extremely sensitive to their local mechanical environment. Indeed, it appears that they try to offset complexities in geometry and applied loads with heterogeneous material properties in order to promote a ‘mechanical homeostasis’. To quantify such heterogeneities, there is a pressing need of novel hybrid experimental-computational methods that involve a drastic change in the experimental approach. In fact, whereas common testing protocols for engineering materials characterization seek to simplify the data analysis by focusing on regions of homogeneity on standard shaped samples, biomechanics demands new methods that enable regional varying material properties to be assessed, often given the native geometry of the tissue or organ.
This talk aims to give an overview on problem-specific experimental protocols based on Fringe Projection and Digital Image Correlation that have been recently developed to collect dense sets of 3D shape and deformation data on biological parts. In particular, the technical challenges associated with the in-vitro measurement of the overall shape and deformation of complex-shaped biological structures under physiological loads will be discussed together with their implications in terms of accuracy and precision of the measured data. Finally, the potential utility of the full-field experimental data obtainable with these approaches will be illustrated presenting the quantification of the regionally varying mechanical properties of biological parts with hybrid inverse characterization methods.