To evaluate the dynamic range of tissue imaged by elastography, the mechanical behavior of breast and prostate tissue samples subject to compression loading has been investigated. A model for the loading was validated and used to guide the experimental design for data collection. The model allowed the use of small samples that could be considered homogeneous; this assumption was confirmed by histological analysis. The samples were tested at three strain rates to evaluate the viscoelastic nature of the material and determine the validity of modeling the tissue as an elastic material for the strain rates of interest. For loading frequencies above 1 Hz, the storage modulus accounted for over 93 percent of the complex modulus. The data show that breast fat tissue has a constant modulus over the strain range tested while the other tissues have a modulus that is dependent on the strain level. The fibrous tissue samples from the breast were found to be 1 to 2 orders of magnitude stiffer than fat tissue. Normal glandular breast tissue was found to have an elastic modulus similar to that of fat at low strain levels, but the modulus of the glandular tissue increased by an order of magnitude above fat at high strain levels. Carcinomas from the breast were stiffer than the other tissues at the higher strain level; intraductal in situ carcinomas were like fat at the low strain level and much stiffer than glandular tissue at the high strain level. Infiltrating ductal carcinomas were much stiffer than any of the other breast tissues. Normal prostate tissue has a modulus that is lower than the modulus of the prostate cancers tested. Tissue from prostate with benign prostatic hyperplasia (BPH) had modulus values significantly lower than normal tissue. There was a constant but not significant difference in the modulus of tissues taken from the anterior and posterior portions of the gland.

The basic principles of using sonographic techniques for imaging the elastic properties of tissues are described, with particular emphasis on elastography. After some preliminaries that describe some basic tissue stiffness measurements and some contrast transfer limitations of strain images are presented, four types of elastograms are described, which include axial strain, lateral strain, modulus and Poisson's ratio elastograms. The strain filter formalism and its utility in understanding the noise performance of the elastographic process is then given, as well as its use for various image improvements. After discussing some main classes of elastographic artefacts, the paper concludes with recent results of tissue elastography in vitro and in vivo.

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Elastography is a method that can ultimately generate several new kinds of images, called elastograms. As such, all the properties of elastograms are different from the familiar properties of sonograms. While sonograms convey information related to the local acoustic backscatter energy from tissue components, elastograms relate to its local strains, Young's moduli or Poisson's ratios. In general, these elasticity parameters are not directly correlated with sonographic parameters, i.e. elastography conveys new information about internal tissue structure and behavior under load that is not otherwise obtainable. In this paper we summarize our work in the field of elastography over the past decade. We present some relevant background material from the field of biomechanics. We then discuss the basic principles and limitations that are involved in the production of elastograms of biological tissues. Results from biological tissues in vitro and in vivo are shown to demonstrate this point. We conclude with some observations regarding the potential of elastography for medical diagnosis.

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Ultrasound elastography produces strain images of compliant tissues under quasi-static compression. In axial-shear strain elastography, the local axial-shear strain resulting from application of quasi-static axial compression to an inhomogeneous material is imaged. The overall hypothesis of this work is that the pattern of axial-shear strain distribution around the inclusion/background interface is completely determined by the bonding at the interface after normalization for inclusion size and applied strain levels, and that it is feasible to extract certain features from the axial-shear strain elastograms to quantify this pattern. The mechanical model used in this study consisted of a single stiff circular inclusion embedded in a homogeneous softer background. First, we performed a parametric study using finite-element analysis (FEA) (no ultrasound involved) to identify possible features that quantify the pattern of axial-shear strain distribution around an inclusion/background interface. Next, the ability to extract these features from axial-shear strain elastograms, estimated from simulated pre- and post-compression noisy RF data, was investigated. Further, the feasibility of extracting these features from in vivo breast data of benign and malignant tumors was also investigated. It is shown using the FEA study that the pattern of axial-shear strain distribution is determined by the degree of bonding at the inclusion/background interface. The results suggest the feasibility of using normalized features that capture the region of positive and negative axial-shear strain area to quantify the pattern of the axial-shear strain distribution. The simulation results showed that it was feasible to extract the features, as identified in the FEA study, from axial-shear strain elastograms. However, an effort must be made to obtain axial-shear strain elastograms with the highest signal-to-noise ratio (SNR(asse)) possible, without compromising the resolution. The in vivo results demonstrated the feasibility of producing and extracting features from the axial-shear strain elastograms from breast data. Furthermore, the in vivo axial-shear strain elastograms suggest an additional feature not identified in the simulations that may potentially be used for distinguishing benign from malignant tumors-the proximity of the axial-shear strain regions to the inclusion/background interface identified in the sonogram.