Abstract:  Biological soft tissue elasticity imaging using ultrasound is studied in this paper. Ordinary ultrasonic imaging use acoustic impedance as the imaging parameters, which can only reflect the change and the distribution of acoustic properties of tissue. When the lesion acoustic impedance changes very little while the elasticity varies much, it is necessary to detect the change in both the acoustic and the mechanical properties of tissue using ultrasound elasticity imaging. It has been more than twenty years since the concept of elastic imaging was put forward. In order to realize the elastic properties of the soft tissue of the real-time, fast, high resolution imaging, there has been much research work on theory and clinical experiment. Elasticity imaging gradually becomes the focus of ultrasound imaging in medical field.
The principle of ultrasonic elasticity imaging mainly includes the traditional elasticity imaging, radiation force imaging and shear wave imaging. The traditional elasticity imaging is a static compression method based on elasticity, in which the global elastic distribution of tissue can be obtained by strain estimation. The previous research mainly includes the principle, algorithm, signal processing, noise suppression and inverse problems etc. Radiation force imaging and shear wave imaging are new imaging methods based on acoustic radiation force, in which the local elastic information nearby the acoustic focusing can be obtained. Moving the focus position can obtain the global distribution of elastic properties.
In this paper, the simulation on ultrasound elasticity imaging of biological soft tissue by using finite element method was studied. By changing the internal distribution and the values of Young’s modulus, the displacement and strain distribution in different cases can be calculated and the simulation results are analyzed. In previous simulation and experimental study, soft tissue is considered as linear elastic model. In this paper, the tissue model is revised by adding consideration of biological tissue viscoelasticity. The simulation results coincide well with the real situation using this optimization of the organizational model. Simulation results show that the use of micro-strain compression method can be realized from the distribution of elastic tissue imaging, and the simulation results by considering viscoelasticity show that the stress, strain and displacement in the tissue are functions of time.