Material characterization of in vivo and in vitro porcine brain using shear wave elasticity.

Realistic computer simulation of closed head trauma requires accurate mechanical properties of brain tissue, ideally in vivo. A substantive deficiency of most existing experimental brain data is that properties were identified through in vitro mechanical testing. This study develops a novel application of shear wave elasticity imaging to assess porcine brain tissue shear modulus in vivo. Shear wave elasticity imaging is a quantitative ultrasound technique that has been used here to examine changes in brain tissue shear modulus as a function of several experimental and physiologic parameters. Animal studies were performed using two different ultrasound transducers to explore the differences in physical response between closed skull and open skull arrangements. In vivo intracranial pressure in four animals was varied over a relevant physiologic range (2-40 mmHg) and was correlated with shear wave speed and stiffness estimates in brain tissue. We found that stiffness does not vary with modulation of intracranial pressure. Additional in vitro porcine specimens (n = 14) were used to investigate variation in brain tissue stiffness with temperature, confinement, spatial location and transducer orientation. We observed a statistically significant decrease in stiffness with increased temperature (23%) and an increase in stiffness with decreasing external confinement (22-37%). This study determined the feasibility of using shear wave elasticity imaging to characterize porcine brain tissue both in vitro and in vivo. Our results underline the importance of temperature- and skull-derived boundary conditions to brain stiffness and suggest that physiologic ranges of intracranial pressure do not significantly affect in situ brain tissue properties. Shear wave elasticity imaging allowed for brain material properties to be experimentally characterized in a physiologic setting and provides a stronger basis for assessing brain injury in computational models.