  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.
