Augmented Region-Growing-Based Motion Tracking Using Bayesian Inference and Local Polynomial Fitting for Quasi-Static Ultrasound Elastography
Ultrasound elastography is a non-invasive imaging method for assessing tissue stiffness and has been used in clinics in the examination of breast,prostate,and abdominal organs.In ultrasound elastography,speckle tracking is a crucial step.The block matching method-based motion estimation and their variants(such as guided displacement tracking algorithm)are commonly used.However,it often introduces peak-hopping errors during the imaging process due to signal de-correlation caused by out-of-plane probe or unrelated physiological motion,resulting in poor quality of the estimated displacement and corresponding strain images generated by such methods.Based on the principle of tissue motion continuity,this study proposed a motion tracking algorithm(BRGMT-LPF)that incorporated Bayesian inference and local polynomial fitting(LPF)into a region-growing motion tracking(RGMT)framework.Firstly,the proposed approach replaced the traditional cross-correlation with the maximum posterior probability.Secondly,LPF was applied to remove and update the peak-hopping or wrong estimated displacement point.The proposed approach was compared with conventional RGMT algorithm,the RGMT with LPF,and the RGMT with Bayesian inference(BRGMT)on the computer-simulated and in vivo ultrasound data.Experimental results showed that on 10 pairs of ultrasound data simulated by finite element software and FIELD Ⅱ,BRGMT-LPF achieved the lowest average absolute error(MAE)of 0.1699(at least 0.25％reduction)and the highest contrast-to-noise ratio(CNR)of 1.1625(at least 4％increase).On 16 pairs of vector data collected from patients with pathologically confirmed breast tumors,BRGMT-LPF obtained the highest CNR of 1.50(at least 0.37％increase)and the highest motion compensation cross-correlation(MCCC)of 0.84(at least 9.4％increase).In conclusion,the proposed method could be used to improve the image quality of ultrasonic elastography and displacement-based modulus reconstruction.
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