Obtaining material properties for biological tissues using inertial microcavitation rheometry
Abstract
Digital engineering harnesses predictive computational modeling for iterative engineering de-sign that minimizes the need for expens,ive testing, and this approach has been successfully deployed in the design of complex engineering systems, e.g., vehicles, such as,ships and planes. However, this process neglects loads and deformations applied to human occupants, which is a crucial aspect of pre,dicting potential injuries and hence occupant safety. For example, traumatic brain injuries can be caused by blast or blunt-impact l,oads to the head and are a pervasive and debilitating injury amongst warghters. A key impediment to predictive digital engineering,involving the mechanical interactions between humans and engineered systems is the high-strain-rate constitutive behavior of biologi,cal tissues, which are required as an input but remain a source of uncertainty. Therefore, there is a need for improved mechanical c,haracterization of biological tissues over a broad range of strain rates. Calibrated, predictive constitutive models for soft biolog,ical tissue materials are currently lacking because applying traditional high-strain-rate material characterization techniques (e.g.,, Kolsky bar or Taylor plate-impact testing) to soft materials is tremendously challenging. To address this need, a new soft-materia,l characterization technique has been developed using measurements of the dynamics of isolated bubbles generated by laser or acousti,c pulses. Using high-speed imaging of the bubble dynamics along with an isotropic theoretical cavitation modeling framework provides, a route to high-strain-rate soft material characterization, and this approach has been shown to be able to discern mechanical prope,rties at previously undocumented strain-rates in materials as soft as a few kPa. In the proposed project, this promising approach wi,ll be lever-aged and applied to obtaining calibrated high-strain-rate constitutive models for several types of biological tissue. Fi,rst, using data from laser-induced cavitation experiments in tissue from several dierent anatomical regions of pig brains (white ma,tter, gray matter, midbrain, hippocampus, etc.) generated by collaborators, numerical simulations of the cavitation process will be,performed, and model predictions will be t to the experimental data in order to determine the high-strain-rate con-stitutive behavi,or of the dierent regions of pig brain tissue. Then, additional tissues that may be characterized using the cavitation rheometry te,chnique, such as kidney and liver tissue, will also be considered. Finally, the theoretical cavitation modeling framework will be ex,tended to account for anisotropic material behavior and used to determine whether information about material anisotropy may be robus,tly obtained using cavitation rheometry. Successful completion of the proposed work would provide the rst application of the cavita,tion-based characterization approach to biological tissue and the rst set of robust, large-strain, high-strain-rate material proper,ties for brain, kidney, and liver tissue. This information may then be used in predictive digital engineering, which will enable imp,roved strategies for mitigating and preventing injuries both in the armed forces and amongst civilians (e.g., in sports-related inju,ries). (approved for public release)
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 08, 2022
- Source ID
- N000142212094
Entities
People
- David L. Henann
Organizations
- Brown University
- Office of Naval Research
- United States Navy