Nonlinear elasticity imaging

In tissue the Young´s modulus cannot be assumed constant as a function of preload. For example, direct mechanical measurements on human prostate show up to a threefold increase in Young´s modulus over a 10% deformation. In conventional elasticity imaging these effects produce strain dependent elastic contrast. Ignoring these effects generally leads to sub-optimal contrast (stiffer tissues at lower strain are contrasted against softer tissues at higher strain), but measuring the nonlinear behavior may enhance tissue differentiation. A simulated agar-gelatin phantom is deformed by 13%, and all interframe displacement data brought back to the geometry of the first frame to form a 3-D data set (depth, lateral, and preload dimensions). Data are fitted to a 3-D second order polynomial model for each pixel that adjusts for deformation irregularities. Reconstructed frame to frame strain images using this model result in improved contrast to noise ratios (CNR) at all preload levels, without any sacrifice in spatial resolution. From the same model strain hardening at all preload levels can be extracted. This is an independent contrast mechanism. It´s maximum CNR occurs at 5.8% preload, and is almost a 70% improvement over the best case (preload 12.8%) CNR for frame to frame strain reconstruction. Finally the technique is also applied to a real phantom. Results show that modeling of the nonlinear elastic behavior has the potential to both increase detectability in elasticity imaging and provide a new, independent mechanism for tissue differentiation.