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.
[1] Shiina T, Nightingale KR, Palmeri ML, et al.Wfumb guidelinesand recommendations for clinical use of ultrasound elastography: Part1: basic principles and terminology[J]. Ultrasound in Medicine & Biology, 2015, 41(5):1126-1147. [2] Oberai AA, Nachiket HG, Sevan G, et al.Linear andnonlinear elasticity imaging of soft tissue in vivo: demonstration offeasibility[J]. Physics in Medicine and Biology, 2009, 54(5):1191-1207. [3] Hall TJ, Zhu YN, Candace SS.In vivo real-time freehand palpation imaging[J]. Ultrasound in Medicine & Biology, 2003, 29(3):427-435. [4] Burnside ES, Hall TJ, Sommer AM, et al.Differentiating benign from malignant solid breast masses with US strain imaging[J]. Radiology, 2007, 245(2):401-410. [5] McCormick M, Rubert N, Varghese T. Bayesian regularization applied to ultrasound strain imaging[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(6):1612-1620. [6] Byram B, Trahey GE, Palmeri M.Bayesian speckle tracking. Part I: an implementable perturbation to the likelihood function for ultrasound displacement estimation[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2012, 60(1): 132-143. [7] Dumont D, Palmeri M, Eyerly S, et al.Feasibility of using a generalized-Gaussian Markov random field prior for Bayesian speckle tracking of small displacements[J]. IEEE International Ultrasonics Symposium, 2014:1845-1848. [8] Zhu Y, Hall TJ.A modified block matching method for real-time freehand strain imaging[J]. Ultrasonic Imaging, 2002, 24(3):161-176. [9] Jiang, J and Timothy J. Hall. A parallelizable real-time motion tracking algorithm with applications to ultrasonic strain imaging[J]. Physics in Medicine and Biology, 2007, 52(13), 3773-3790. [10] Zahiri-Azar R, Salcudean SE.Motion estimation in ultrasound images using time domain cross correlation with prior estimates[J]. IEEE Transactions on Biomedical Engineering, 2006, 53(10), 1990-2000. [11] Wang Y, Jiang J, Hall TJ.A 3-D region-growing motion-tracking method for ultrasound elasticity imaging[J]. Ultrasound in Medicine & Biology, 2018, 44(8):1638-1653. [12] 张耀楠, 孙婷婷. 带有预测的二维超声射频信号快速位移估计方法的研究[J]. 中国医疗设备, 2016, 31(5):30-35. [13] 欧阳灵, 刘晓宙, 刘杰惠,等. 利用先验估计自适应加窗的准静态超声弹性成像位移估计算法[J]. 声学学报, 2016, 41(5): 597-604. [14] 李伟方. 超声弹性成像全局位移场估计算法研究[D]. 武汉:武汉理工大学, 2013. [15] 田志鑫, 田淑爱, 丁婷. 一种改进的基于二维自相关的超声弹性成像方法[J]. 测试技术学报, 2020, 34(4): 338-343. [16] Peng B, Wang Y, Hall TJ, et al.A GPU-accelerated 3-D coupled subsample estimation algorithm for volumetric breast strain elastography[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2017, 64(4):694-705. [17] 彭博, 谌勇, 刘东权. 基于GPU的超声弹性成像并行实现研究[J]. 光电工程, 2013, 40(5), 97-105. [18] Ye,Guolianget al. A model-based displacement outlier removal algorithm for ultrasonic temperature estimation[J]. IEEE Ultrasonics Symposium ,2008, 66(5): 2080-2083. [19] Chen L, Treece GM, Lindop JE, et al.A quality-guided displacement tracking algorithm for ultrasonic elasticity imaging[J]. Medical Image Analysis, 2009, 13(2):286-296. [20] Hall TJ, Zhu Y, Spalding CS.In vivo real-time freehand palpation imaging[J]. Ultrasound in Medicine & Biology, 2003, 29(3):427-435. [21] Peng B, Wang Y, Yang WJ, et al.Relative elastic modulus imaging using sector ultrasound data for abdominal applications: an evaluation of strategies and feasibility[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2016, 63(9):1432-1440. [22] Peng B, Yang T, He T, et al.Augmented region-growing-based motion tracking using Bayesian inference for quasi-static ultrasound elastography[C] // 2020 IEEE International Conference on Image Processing. Piscataway: IEEE, 2020:2960-2964. [23] Byram B, Trahey GE, Palmeri M.Bayesian speckle tracking. Part I: an implementable perturbation to the likelihood function for ultrasound displacement estimation[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2012, 60(1):132-143. [24] 彭博, 罗莎莎, 杨烽, 等. 基于和表的互相关计算方法在超声弹性成像中的性能分析[J]. 光电工程, 2019, 46(6): 180437.
