北京生物医学工程

基于经验模态分解的超声零差K成像评估肝纤维化研究

Ultrasonic evaluation of hepatic fibrosis with homodyned K imaging based on empirical mode decomposition

作者：张奇宇吴水才崔博翔周著黄

单位：北京工业大学环境与生命学部（北京 100124） 台湾长庚大学医学院(中国台湾桃园  33302) 通信作者：周著黄。副研究员。E-mail: zhouzh@bjut.edu.cn

分类号：R318.04

出版年·卷·期（页码）：2022·41·2（125-133）

摘要：

目的为提高超声零差K成像评估肝纤维化的性能，提出基于经验模态分解的超声背散射零差K成像评估肝纤维化方法，利用经验模态分解技术，消除肝实质等噪声信号对肝纤维化信号的影响。方法首先，将采集到的43例临床肝纤维化的超声背散射信号(F0 = 14, F1 = 10, F2 = 6, F3 = 2, F4 = 11）进行经验模态分解，然后分别将分解后的第一本征模态函数和第二本征模态函数经包络检测、滑动窗口、零差K模型参数估算等处理，计算得到感兴趣区域内的零差K模型参数k和α矩阵，通过扫描变换得到零差K参数图像，最后采用参数k和α对肝纤维化进行评估。结果采用经验模态分解技术提高了超声零差K成像诊断肝纤维化的性能。参数k在诊断肝纤维化≥F1即有无肝纤维化时提高较为明显，平均受试者工作特征曲线下面积提高至0.68，参数α在诊断肝纤维化程度≥F1时，平均受试者工作特征曲线下面积提高至0.82。结论经验模态分解技术有效减少了肝实质等非目标背散射信号对肝纤维信号的影响，提高了超声零差K成像检测肝纤维化的性能，并且在判别有无肝纤维化时性能最佳；在肝纤维化评估效果方面，参数α优于参数k。

Objective To improve the performance of ultrasound homodyned K imaging in the evaluation of liver fibrosis, in this study, ultrasonic backscattering homodyned K imaging based on empirical mode decomposition was proposed for assessment of liver fibrosis. The empirical mode decomposition technique was used to eliminate the influence of noise signals such as liver parenchyma on hepatic fibrosis signals. The effect of liver fibrosis assessment before and after empirical mode decomposition was compared. Methods The ultrasonic backscattering signals collected from 43 cases of clinical liver fibrosis (F0 = 14, F1 = 10, F2 = 6, F3 = 2, F4 = 11) were decomposed by empirical mode decomposition. Then, the decomposed first intrinsic mode function and second intrinsic mode function signals were processed by envelope detection, sliding window, and homodyned K model parameter estimation. Thus, the homodyned K model parameters k and α matrices were estimated within the regions of interest. Finally, the homodyned K parametric image was obtained by digital scan conversion, Finally, parameters k and α were used for evaluation of liver fibrosis. Results The results showed that the performance of ultrasonic homodyned K imaging in the diagnosis of liver fibrosis was improved by using the empirical mode decomposition technique. Parameter k significantly increased the performance when diagnosing liver fibrosis ≥ F1, and the average area under the receiver operating characteristic curve increased to 0.68. Parameter α yielded an average AUC of 0.82 when diagnosing liver fibrosis ≥ F1. Conclusions The empirical mode decomposition technique effectively reduced the influence of non-target backscattering signals such as liver parenchyma on liver fiber signals. It improved the performance of ultrasonic homodyned K imaging in detecting liver fibrosis, and had the best performance in determining whether there is liver fibrosis. Parameter α was better than parameter k in the ultrasonic evaluation of liver fibrosis.

参考文献：

[1]?Asrani SK, Devarbhavi H, Eaton J, et al. Burden of liver diseases in the world[J]. Journal of Hepatology, 2019, 70(1): 151-171.

[2]?Zheng R, Qu C, Zhang S, et al. Liver cancer incidence and mortality in China: Temporal trends and projections to 2030[J]. Chinese Journal of Cancer Research, 2018, 30(6): 571-579.

[3]?Bravo AA, Sheth SG, Chopra S.?Liver biopsy[J]. New England Journal of Medicine, 2001, 344(7): 495-500.

[4]?Bedossa P, Dargère D, Paradis V, et al. Sampling variability of liver fibrosis in chronic hepatitis C[J]. Hepatology, 2003, 38(6): 1449-1457.

[5]?Destrempes F, Cloutier G. A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope[J]. Ultrasound in Medicine and?Biology, 2010, 36(7): 1037-1051.

[6]?Zhou Z, Gao A, Zhang Q, et al. Ultrasound backscatter envelope statistics parametric imaging for liver fibrosis characterization: a?review[J]. Ultrasonic Imaging, 2020, 42(2): 92-109.

[7]?Zhou Z, Wu W, Wu S, et al. A review of ultrasound tissue characterization with mean scatterer spacing[J]. Ultrasonic Imaging, 2017, 39(5): 263-282.

[8]?欧阳亚丽, 周著黄, 吴水才, 等. 超声零差K分布模型仿真研究[J]. 北京生物医学工程, 2019, 38(2): 119-125.

Ouyang YL, Zhou ZH, Wu SC, et al. Simulation study on ultrasound homodyned-K distribution model[J]. 北京生物医学工程, 2019, 38(2): 119-125.

[9]?Oelze ML, Mamou J. Review of quantitative ultrasound: envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2016, 63(2): 336-351.

[10]?Cristea A, Collier N, Franceschini E, et al. Quantitative assessment of media concentration using the homodyned K distribution[J]. Ultrasonics, 2020, 101: 105986.

[11]?Zhou Z, Wu S, Wang CY, et al. Monitoring radiofrequency ablation using real-time ultrasound Nakagami imaging combined with frequency and temporal compounding techniques[J]. PLoS One, 2015, 10(2): e0118030.

[12]?Zhang S, Shang S, Han Y, et al. Ex vivo and in vivo monitoring and characterization of thermal lesions by high-Intensity focused ultrasound and microwave ablation using ultrasonic Nakagami imaging[J]. IEEE Transactions on Medical Imaging, 2018, 37(7): 1701-1710.

[13]?Zhou Z, Zhang Q, Wu W, et al. Hepatic steatosis assessment using ultrasound homodyned-K parametric imaging: the effects of estimators[J]. Quantitative Imaging in Medicine and Surgery, 2019, 9(12): 1932-1947.

[14]?Tsai YW, Zhou Z, Gong CSA, et al. Ultrasound detection of liver fibrosis in individuals with hepatic steatosis using the homodyned K distribution[J]. Ultrasound in Medicine & Biology, 2021, 47(1): 84-94.

[15]?Hao X, Bruce CJ, Pislaru C, et al. Characterization of reperfused infarcted myocardium from high-frequency intracardiac ultrasound imaging using homodyned K distribution[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2002, 49(11): 1530-1542.

[16]?Mamou J, Coron A, Oelze ML, et al. Three-dimensional high-frequency backscatter and envelope quantification of cancerous human lymph nodes[J]. Ultrasound in Medicine & Biology, 2011, 37(3): 345-357.

[17]?Byra M, Nowicki A, Wróblewska-Piotrzkowska H, et al. Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters[J]. Medical Physics, 2016, 43(10): 5561.

[18]?Roy-Cardinal MH, Destrempes F, Soulez G, et al. Assessment of carotid artery plaque components with machine learning classification using homodyned-K parametric maps and elastograms[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2019, 66(3): 493-504.

[19]?Zhou Z, Tai DI, Wan YL, et al. Hepatic steatosis assessment with ultrasound small-window entropy imaging[J]. Ultrasound in Medicine & Biology, 2018, 44(7): 1327-1340.

[20]?Zhou Z, Fang J, Cristea A, et al. Value of homodyned K distribution in ultrasound parametric imaging of hepatic steatosis: an animal study[J]. Ultrasonics, 2020, 101: 106001.

[21]?张奇宇,吴水才,崔博翔,等. 超声背散射零差K成像评估肝纤维化[C]//2019年上海-西安声学学会第六届声学学术交流会议论文集.上海：上海-西安声学学会，2019：12-15.

[22]?Zhou Z, Wu S, Lin MY, et al. Three-dimensional visualization of ultrasound backscatter statistics by window-modulated compounding Nakagami imaging[J]. Ultrasonic Imaging, 2018, 40(3): 171-189.

[23]?Son JY, Lee JY, Yi NJ, et al. Hepatic steatosis: assessment with acoustic structure quantification of US imaging[J]. Radiology, 2016, 278(1): 257-264.

[24]?Huang N, Shen Z, Long SR, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A?Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995.

[25]?Tsui PH, Chang CC, Huang NE. Noise-modulated empirical mode decomposition[J]. Advances in Adaptive Data Analysis, 2010, 2(1): 25-37.

[26]?刘备, 董胡, 钱盛友. 基于经验模态分解与小波分析的超声信号降噪方法[J]. 测试技术学报, 2018, 32(5): 422-428.

??Liu B, Dong H, Qian SY. Ultrasonic signal denoising method based on empirical mode decomposition?and wavelet analysis[J]. Journal of Test and Measurement Technology, 2018, 32(5): 422-428.

[27]?Dutt V, Greenleaf JF. Ultrasound echo envelope analysis using a homodyned K distribution signal model[J]. Ultrasonic Imaging, 1994, 16(4): 265-287.

[28]?Destrempes F, Porée J, Cloutier G. Estimation method of the homodyned K-distribution based on the mean intensity and two log-moments[J]. SIAM Journal on Imaging Sciences, 2013, 6(3): 1499-1530.

[29]?Castera L. Noninvasive methods to assess liver disease in patients with hepatitis B or C[J]. Gastroenterology, 2012, 142(6): 1293-1302.e4.

[30]?Liang XE, Chen YP, Zhang Q, et al. Dynamic evaluation of liver stiffness measurement to improve diagnostic accuracy of liver cirrhosis in patients with chronic hepatitis B acute exacerbation[J]. Journal of Viral Hepatitis, 2011, 18(12): 884-891.

[31]?Thiele M, Detlefsen S, M?ller LS, et al. Transient and 2-dimensional shear-wave elastography provide comparable assessment of alcoholic liver fibrosis and cirrhosis[J]. Gastroenterology, 2016, 150(1): 123-133.

[32]?Smith AD, Porter KK, Elkassem AA, et al. Current imaging techniques for noninvasive staging of hepatic fibrosis[J]. American Journal of Roentgenology, 2019, 213(1): 77-89.

[33]?Kr?mer C, Jaspers N, Nierhoff D, et al. Acoustic structure quantification ultrasound software proves imprecise in assessing liver fibrosis or cirrhosis in parenchymal liver diseases[J]. Ultrasound in Medicine & Biology, 2014, 40(12): 2811-2818.

[34]?Karlas T, Berger J, Garnov N, et al. Estimating steatosis and fibrosis: comparison of acoustic structure quantification with established techniques[J]. World Journal of Gastroenterology, 2015, 21(16): 4894-4902.

[35]?Tsui PH, Ma HY, Zhou Z, et al. Window-modulated compounding Nakagami imaging for ultrasound tissue characterization[J]. Ultrasonics, 2014, 54(6): 1448-1459.

[36]?Tsui PH, Wan YL, Tai DI, et al. Effects of estimators on ultrasound Nakagami imaging in visualizing the change in the backscattered statistics from a Rayleigh distribution to a pre-Rayleigh distribution[J]. Ultrasound in Medicine & Biology, 2015, 41(8): 2240-2251.

[37]?Tsui PH, Ho MC, Tai DI, et al. Acoustic structure quantification by using ultrasound Nakagami imaging for assessing liver fibrosis[J]. Scientific Reports, 2016, 6: 33075.

[38]?Higuchi T, Hirata S, Yamaguchi T, et al. Quantitative evaluation of liver fibrosis using multi-Rayleigh model with hypoechoic component[J]. Japanese Journal of Applied Physics, 2013, 52(7S): 07HF19.

服务与反馈：

【文章下载】【加入收藏】

提示：您还未登录，请登录！点此登录