Augmented region-growing-based Motion Tracking using Bayesian inference for quasi-static ultrasound elastography

Tissue motion tracking is a critically important step for many ultrasound elastography applications. In this study, we are particularly interested in evaluating motion tracking strategies for large deformation quasi-static elastography. In this study, Bayesian inference is incorporated into a region-growing motion estimation framework and we named the proposed tracking algorithm as a region-growing Bayesian motion tracking (RGBMT) algorithm. Basically, we replace signal correlation by a maximum posterior probability density function to perform motion tracking. Using a computer-simulated phantom and one set of human subject ultrasound data with pathologically-confirmed breast cancer, the proposed RGBMT algorithm was compared to the original region-growing motion tracking algorithm. Our preliminary data suggested that the addition of Bayesian inference is useful in terms of improving the accuracy of motion tracking. Results from both the numerical phantom and in vivo ultrasound data set showed that there are fewer tracking errors in axial displacement and strain images obtained from the proposed RGBMT algorithms. That explained why the contrast-to-noise (CNR) values were higher and the breast tumor on the reconstructed modulus image was better visualized.