Sonoelastography is an ultrasound imaging technique where low amplitude, low-frequency shear waves (less than 0.1 mm displacement and less than 1 kHz frequency) are propagated through internal organs, while real-time Doppler techniques are used to image the resulting vibration pattern. When a discrete hard inhomogeneity, such as a tumour, is present within a region of soft tissue, a decrease in the vibration amplitude will occur at its location. This forms the basis for tumour detection using sonoelastography. For three-dimensional (3D) imaging the acquisition of sequential tomographic slices using this technique, combined with image segmentation, enables the reconstruction, quantification and visualization of tumour volumes. Sonoelastography and magnetic resonance images (MRI) of a tissue phantom containing a hard isoechoic inclusion are compared to evaluate the accuracy of this method. The tumour delineation from sonoelastography was found to have good agreement with the tumour from MRI except for a bleeding at one of its ends. Although sonoelastography is still in an experimental phase, the principles behind this imaging modality are explained and some practical aspects of acquiring sonoelastography images are described. Results from a 3D sonoelastography reconstruction of a tissue mimicking phantom and an ex vivo whole prostate specimen are presented.

The shear wave velocity is one of a few important parameters that characterize the mechanical properties of bio-materials. In this paper, two noninvasive methods are proposed to measure the shear velocity by inspecting the shear wave interference patterns. In one method, two shear wave sources are placed on the opposite two sides of a sample, driven by the identical sinusoidal signals. The shear waves from the two sources interact to create interference patterns, which are visualized by the vibration sonoelastography technique. The spacing between the pattern bands equals half of the shear wavelength. The shear velocity can be obtained by taking the product of the wavelength and the frequency. An alternative method is to drive the two vibration sources at slightly different frequencies. In this case, the interference patterns no longer remain stationary. It is proved that the apparent velocity of the moving patterns is proportional to the shear velocity in the medium. Since the apparent velocity of the patterns can be measured by analysing the video sequence, the shear velocity can be obtained thereafter. These approaches are validated by a conventional shear wave time-of-flight approach, and they are accurate within 4% on various homogeneous tissue-mimicking phantoms.

A number of different approaches have been developed to estimate and image the elastic properties of tissue. The biomechanical properties of tissues are vitally linked to function and pathology, but cannot be directly assessed by conventional ultrasound, MRI, CT, or nuclear imaging. Research developments have introduced new approaches, using either MRI or ultrasound to image the tissue response to some stimulus. A wide range of stimuli has been evaluated, including heat, water jets, vibration shear waves, compression, and quasistatic compression, using single or multiple steps or low-frequency (<10 Hz) cyclic excitation. These may seem to be greatly dissimilar, and appear to produce distinctly different types of information and images. However, our purpose in this tutorial is to review the major classes of excitation stimuli, and then to demonstrate that they produce responses that fall within a common spectrum of elastic behavior. Within this spectrum, the major classes of excitation include step compression, cyclic quasistatic compression, harmonic shear wave excitation, and transient shear wave excitation. The information they reveal about the unknown elastic distribution within an imaging region of interest are shown to be fundamentally related because the tissue responses are governed by the same equation. Examples use simple geometry to emphasize the common nature of the approaches.

PURPOSE: To prospectively evaluate the accuracy of 3D sonoelastography for detection of prostate cancer relative to gray scale sonography in vitro.METHODS: Using an Institutional Review Board-approved, HIPAA compliant protocol with informal consent, 19 prostatectomy specimens from patients 46 to 70 years of age with biopsy proven prostate cancer were scanned in 3D using conventional B-scan and sonoelastography using vibrations above 100Hz.Step-sectioned whole-mount histology was utilized to create a 3D volume of the prostate and tumors within it. B-scan ultrasound images and regions of low vibration in the sonoelastography images (hard regions) were formatted in 3D. The lesions in the nineteen cases were analyzed as two groups: G1) pathology-confirmed tumors of 1.0 cc or greater; and G2) pathology-confirmed tumor size less than 1.0 cc. G1 cases were evaluated for B-scan ultrasound and sonoelastography vs. histology as a reference standard and were scored as either a True Positive, a False Positive, a True Negative, or a False Negative. G2 cases were evaluated for sonoelastography only. True positives required 3D lesion correlation between pathology and imaging data. Conventional definitions of accuracy and sensitivity were employed to calculate these statistics. RESULTS: G1 (7 lesions with tumor volume 1.0 cc or greater): Sonoelastography: accuracy of 55%, sensitivity of 71%. B-scan: accuracy of 17%, sensitivity of 29%. Mean tumor size is 3.1cc +/-2.1cc. G2 (22 lesions with tumor volume less than 1.0 cc). Mean tumor size is 0.32 cc +/-0.21 cc. Sonoelastography: accuracy of 34%, sensitivity of 41%, false positives: 6.CONCLUSIONS: Sonoelastography performed considerably better than gray scale sonography in the detection of prostate cancer tumors over 1 cc.