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| Augmented Region-Growing-Based Motion Tracking Using Bayesian Inference and Local Polynomial Fitting for Quasi-Static Ultrasound Elastography |
| Wen Shuojie1, Zhou Jingyu1, Zhou Wenjun1, Jiang Jinfeng2, Peng Bo1,* |
1(School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China);
2(Department of Biomedical Engineering, Michigan Technological University, Houghton, Michigan 49931, USA) |
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Abstract 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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Received: 29 October 2021
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