Image-based mechanical characterization of soft tissue using three dimensional ultrasound

Computational biomechanical models have become integral components in many areas of modern medical care, including diagnostic applications, image-guided procedures, robot-assisted procedures, and surgical simulators. The development of appropriate models for the mechanical behavior of soft tissues is challenging due to the inherent complexities of the material response, and the limitations on testing protocols associated with in vivo settings. Current in vivo soft tissue testing is dominated by indentation due to the simplicity of the instrumentation and low risk of injury associated with the procedure. Much of the information related to the interplay between shear and bulk compliance in the complex deformation field beneath the indenter is lost when capturing the single (time-displacement-force) output of the tool. Supplemental experimental methods are necessary for well-conditioned characterization of the tissue response. Image-based methods are a promising solution, as they provide the means for noninvasive in vivo measurement of the tissue response with improved sensitivity and uniqueness of the recovered material parameters. A constitutive inverse modeling framework is presented, relying on conventional indentation testing along with real-time three dimensional ultrasound imaging of the internal tissue deformation. The internal organ deformation field is estimated with a novel, mechanically regularized nonrigid image registration algorithm. A physically- based visco-elastic constitutive model of the liver response is developed and its material parameters are estimated within the proposed inverse modeling framework. Three perfused porcine livers were characterized using tests representative of surgical manipulation, including cyclic loading tests spanning applied strain rates between 0.01 s -1 and 1.0 s -1 and stress relaxation tests. The proposed model and the identified material parameters offer good fit to the experimental response and show good predictive capability for alternative loading histories. The proposed material testing methods are independent of imaging modality and constitutive law, suggesting potential applications for other tissues and scales (i.e. nanoindentation, confocal microscopy, etc.).