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Ultrasound elastography has become a wellknown optional imaging method for the diagnosis of tissue abnormalities in various body parts. It images the elasticity of compliant tissues by estimating the local displacements and strains using pre- and post-compression RF echo signals. In this paper, taking the RF signal as image intensity and RF samples as pixels, we present a motion estimation framework to compute the axial tissue displacements and strains. This method takes advantage of both the block matching algorithm (BMA) and local optical flow techniques. For two frames of RF signals, coarse motion estimates are first computed using BMA. The motion estimates obtained are then used to warp the first frame toward the second one, thus making the warped frame more spatially correlated to the second one. Next, the Lucas-Kanade optical flow method is employed to compute the residual motion between the warped frame and the original second frame, with inherent sub-pixel precision. Finally, the displacements from the two steps are combined. The warp-and-refine procedure can be iterated if the residual motion is larger than a predefined empirical threshold. To test its feasibility, we first applied the method to simulated data. The results show that our method is robust to relatively large motions and is capable of generating accurate motion estimation with subsample spatial resolution. These methods have been deployed and are being tested on a commercialized ultrasound machine that previously did not have elastography functions. Quality real-time display of elastography along with freehand scanning has been accomplished. The proposed framework provides an alternative method for motion estimation with good performance, and it can potentially be improved using hardware to realize the BMA.