An Adaptive Strain Estimation Algorithm Using Short Term Cross Correlation Kernels and 1.5D Lateral Search

Adaptive stretching, where the post compression signal is iteratively stretched to maximize the correlation between the pre and post compression rf echo frames, has demonstrated superior performance compared to gradient based methods. At higher levels of applied strain however, adaptive stretching suffers from decorrelation noise and the image quality deteriorates significantly. Reducing the size of correlation windows have previously showed to enhance the performance in a speckle tracking algorithm but a correlation filter was required to prevent peak hopping errors. In this paper, we present a novel strain estimation algorithm which utilizes an array of overlapping short term cross correlation kernels which are about one-fourth the size of a typical large kernel, to implement an adaptive stretching algorithm. Our method does not require any supplementary correlation filter to prevent false peak errors. Additionally, a lateral search is incorporated using 1.5D algorithm to account for the mechanically induced lateral shift. To validate the efficacy of our proposed method we analyzed the results using simulation and in-vivo data of breast tumors. Our proposed method demonstrated a superior performance compared to classical adaptive stretching algorithm in both qualitative and quantitative assessment. Strain SNRe, CNRe and image resolution are found to improve significantly. Lesion's shape and boundary are more clearly depicted. The results of improvement are clearly evident at higher levels of applied strain.