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A Fast Hybrid Algorithm Combining Regularized Motion Tracking and Predictive Search for Reducing the Occurrence of Large Displacement Errors

机译:一种快速的混合算法结合了正则化运动跟踪和预测搜索来减少大位移误差的发生

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摘要

A hybrid approach that inherits both the robustness of the regularized motion tracking approach and the efficiency of the predictive search approach is reported. The basic idea is to use regularized speckle tracking to obtain high quality seeds in an explorative search that can be used in the subsequent intelligent predictive search.The performance of the hybrid speckle tracking algorithm was compared with three published speckle tracking methods using in vivo breast lesion data. We found that the hybrid algorithm provided higher displacement quality metric values, lower root mean squared errors compared to a locally smoothed displacement field, and higher improvement ratios compared to the classic block-matching algorithm. On the basis of these comparisons, we concluded that the hybrid method can further enhance the accuracy of speckle tracking compared to its real-time counterparts, at the expense of slightly higher computational demand.

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