Real time tissue elasticity imaging using the combined autocorrelation method

Tsuyoshi Shiina, Naotaka Nitta, Ei Ueno, Jeffrey C. Bamber

The elastic properties of tissues are expected to provide novel information for use in diagnosing pathologic changes in tissues and discriminating between malignant and benign tumors. Because it is hard to directly estimate the elastic modulus distribution from echo signals, methods for imaging the distribution of tissue strain under static compression are being widely investigated. Imaging the distribution of strain has proven to be useful for detecting disease tissues on the basis of their differences in elastic properties, although it is more qualitative than elastic modulus distribution. Many approaches to obtaining strain images from echo signals have been proposed. Most of these approaches use the spatial correlation technique, a method of detecting tissue displacement that provides maximum correlation between the echo signal obtained before and the one obtained after compression. Those methods are not suited for real-time processing, however, because of the amount of computation time they require. An alternative approach is a phase-tracking method, which is analogous to Doppler blood flowmetry. Although it can realize the rapid detection of displacement, the aliasing effect prevents its application to the large displacements that are necessary to improve the S/N ratio of the strain image. We therefore developed a more useful technique for imaging tissue elasticity. This approach, which we call the combined autocorrelation (CA) method, has the advantages of producing strain images of high quality with real-time processing and being applicable to large displacements.Numeric simulation and phantom experimentation have demonstrated that this method's capability to reconstruct images of tissue strain distribution under practical conditions is superior to that of the conventional spatial correlation method. In simulation and phantom experimentation, moreover, the image of elastic modulus distribution was also obtained by estimating stress distribution using a three-dimensional tissue model. When the proposed CA method was used to measure breast tumor specimens, the obtained strain images clearly revealed harder tumor lesions that were only vaguely resolved in B-mode images. Moreover, the results indicated the possibility of extracting the pathological characteristics of a tumor, making it useful for determining tumor type. These advantages justify the clinical use of the CA method.