The hypothesis for the proposed finite element model based state space (FEMSS) is that given that the displacements in a living tissue are known (can be provided via MRI, ultrasound etc.) then we can characterize its elastic and viscous properties. The FEMSS is not based on a particular imaging technique in order to acquire the displacements. During this project we will develop an ultrasound array so as to have immediate correlation and validation of the model in house, and we will use MRI images to correlate our data. In order to achieve our goal we propose to (a) Investigate linear and non-linear solid dynamics models for soft tissues, (b) Model and characterize of uncertainties presented in measurements and our system, (c) Develop a phantom virtual laboratory, and to (d) Experimental validation and fine tuning. We will fine-tune our FEMSS model with experiments performed in our labs. If successful the proposed work will provide a consistent, unitary mathematical model for the solution of the forward problem for the non-invasive characterization of living tissues. We anticipate that this work will create enabling technologies for tissue diagnostics with applications in cancer research, remote surgery and athletics. Moreover this multidisciplinary program will bring in tasks from materials science, bioengineering, micro & nano measurements, geometric & material modeling controls, hi-tech computational techniques, and image processing to develop a state of the art tissue diagnostic tool, with unique quantitative measuring capabilities and that is described mathematically to its entirety. Graduate and undergraduate students will be trained in state of the art computational and experimental techniques. We will actively collaborate and train minority students and we will outreach to local communities in order to educate the public about the use of materials modeling in biomedical applications. Results dissemination will occur via internet, journals and conferences.
Intellectual Merit Biomechanical imaging techniques based on acoustic radiation force (ARF) have been developed for soft tissue discrimination and detection of tumors. In most of the ARF imaging techniques, the interrogated region of interest is considered as purely elastic, homogeneous and infinite medium. However, these assumptions highly reduce the mechanical property image contrast of the soft tissue. We have been working on both investigating the limitations of these assumptions and proposing feasible procedure to improve the image contrast in ARF imaging techniques. Our research on the effect of global boundary conditions (GBC) shows the GBC of the medium play an important role in the responses of soft tissue to ARF, which will further affects the image contrast. For example, the response of breast tumor may differ because of its position, e.g., near the rigid rib or some soft, less constrained background. In addition, our research also shows the important role of viscosity in ARF imaging, even in the imaging of elasticity contrast. For example, in harmonic excitation, small displacement amplitude can result from high elasticity, or it can be due to the viscous damping. Without considering the effects of viscosity, the elasticity contrast can be highly reduced. Our research on modeling the ARF imaging in viscoelastic heterogeneous soft tissue demonstrates the limitations of the homogeneous and infinite medium assumption. For heterogeneous media, the responses of the focal region are highly affected by the local heterogeneity, which makes the homogeneous and infinite medium assumption invalid. In order to obtain high image contrast on soft tissue viscoelasticity, we are developing the inverse finite element characterization procedure to overcome the above limitations. The viscoelastic behavior, local heterogeneity, and the acoustic radiation force excitation are all considered and integrated in the finite element model. This framework is also able to evaluate the relative influence of different sources of uncertainty in practical application, which will be carried out via numerical simulations and correlation with experimental results. This study will make the ARF imaging method a more accurate and practical technique in the biomedical imaging field. Broader Impact Several researchers at numerous institutions around the country have been working to develop new ways of detecting abnormal tissues within the body. These methods aim to exploit the mechanical differences between normal and pathological tissue most often witnessed by changes in stiffness. The results from these methods seem promising, but they have shortcomings to be addressed before they can be used as a viable clinical option. We are offering a new technique, fully characterized mathematically, that attempts to bridge the shortcomings of current methods. The work conducted during this award is an interdisciplinary quest to develop an improved device and protocols for in vivo measurement of the anisotropic and viscoelastic properties of tissues. Currently, there is no quantitative determination of the aforementioned properties, thus impeding diagnostic procedures and encouraging unnecessary biopsies. We believe that the accomplishments of this project can expedite the progress of medical diagnostics and imaging, tissue evaluation, and athletics, (muscle evaluation), as well as provide a robust inverse local/global FE methodology for tissue characterization in many diverse fields. During this grant 3 graduate, 2 undergraduate, and 2 high school students were professionally mentored, trained and guided towards engineering careers in academia or industry.
