Monitoring microwave ablation using ultrasound backscatter homodyned K imaging: Comparison of estimators_Journal of Biomedical Engineering

Authors：

SONG Shuang ^1,2 , ZHANG Yinghua ³ ,  ZHOU Zhuhuang ^1,2 ,  WU Shuicai ^1,2

1. Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P.R.China;
2. Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, P.R.China;
3. Beijing You'an Hospital, Capital Medical University, Beijing 100069, P.R.China;

Corresponding author：

ZHOU Zhuhuang, Email: zhouzh@bjut.edu.cn; WU Shuicai, Email: wushuicai@bjut.edu.cn

Keywords：

ultrasound; backscatter; homodyned K distribution; microwave ablation; monitoring

DOI：

10.7507/1001-5515.202003032

Video：

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The feasibility of ultrasound backscatter homodyned K model parametric imaging (termed homodyned K imaging) to monitor coagulation zone during microwave ablation was investigated. Two recent estimators for the homodyned K model parameter, RSK (the estimation method based on the signal-to-noise ratio, the skewness, and the kurtosis of the amplitude envelope of ultrasound) and XU (the estimation method based on the first moment of the intensity of ultrasound, X statistics and U statistics), were compared. Firstly, the ultrasound backscattered signals during the microwave ablation of porcine liver ex vivo were processed by the noise-assisted correlation algorithm, envelope detection, sliding window method, digital scan conversion and color mapping to obtain homodyned K imaging. Then 20 porcine livers’ microwave ablation experiments ex vivo were used to evaluate the effect of homodyned K imaging in monitoring the coagulation zone. The results showed that the area under the receiver operating characteristic curve of the RSK method was 0.77 ± 0.06 (mean ± standard deviation), and that of the XU method was 0.83 ± 0.08 (mean ± standard deviation). The accuracy to monitor the coagulation zone was (86 ± 10)% (mean ± standard deviation) by the RSK method and (90 ± 8)% (mean ± standard deviation) by the XU method. Compared with the RSK method, the Bland-Altman consistency for the coagulation zone estimated by the XU method and that of actual porcine liver tissue was higher. The time for parameter estimation and imaging by the XU method was less than that by the RSK method. We conclude that ultrasound backscatter homodyned K imaging can be used to monitor coagulation zones during microwave ablation, and the XU method is better than the RSK method.

Citation： SONG Shuang, ZHANG Yinghua, ZHOU Zhuhuang, WU Shuicai. Monitoring microwave ablation using ultrasound backscatter homodyned K imaging: Comparison of estimators. Journal of Biomedical Engineering, 2021, 38(3): 520-527. doi: 10.7507/1001-5515.202003032 Copy

1.	An C, Cheng Z, Yu X, et al. Ultrasound-guided percutaneous microwave ablation of hepatocellular carcinoma in challenging locations: oncologic outcomes and advanced assistive technology. Int J Hyperther, 2020, 37(1): 89-100.
2.	Izzo F, Granata V, Grassi R, et al. Radiofrequency ablation and microwave ablation in liver tumors: an update. Oncologist, 2019, 24(10): 1-16.
3.	Perrodin S, Lachenmayer A, Maurer M, et al. Percutaneous stereotactic image-guided microwave ablation for malignant liver lesions. Sci Rep-UK, 2019, 9(1): 1-8.
4.	Simon C J, Dupuy D E, Mayo-Smith W W. Microwave ablation: principles and applications. Radiographics, 2005, 25(S1): S69-S83.
5.	高钦宗, 王志伟, 金征宇. 肝癌热消融治疗后影像学评价的进展. 医学研究杂志, 2018, 47(9): 10-14.
6.	欧阳亚丽, 周著黄, 吴水才, 等. 超声零差K模型仿真研究. 北京生物医学工程, 2019, 38(2): 13-19.
7.	Oelze M L, Mamou J. Review of quantitative ultrasound: envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control, 2016, 63(2): 336-351.
8.	Cristea A, Collier N, Franceschini E, et al. Quantitative assessment of media concentration using the Homodyned K distribution. Ultrasonics, 2020, 101: 105986.
9.	Zhou Z, Wu S, Wang C Y, et al. Monitoring radiofrequency ablation using real-time ultrasound Nakagami imaging combined with frequency and temporal compounding techniques. PLoS ONE, 2015, 10(2): e0118030.
10.	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. IEEE T Med Imaging, 2018, 37(7): 1701-1710.
11.	Destrempes F, Cloutier G. A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope. Ultrasound Med Biol, 2010, 36(7): 1037-1051.
12.	Zhou Z, Zhang Q, Wu W, et al. Hepatic steatosis assessment using ultrasound homodyned-K parametric imaging: the effects of estimators. Quant Imaging Med Surg, 2019, 9(12): 1932-1947.
13.	Byra M, Nowicki A, Wróblewska-Piotrzkowska H, et al. Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters. Med Phys, 2016, 43(10): 5561-5569.
14.	Zhou Z, Fang J, Cristea A, et al. Value of homodyned K distribution in ultrasound parametric imaging of hepatic steatosis: an animal study. Ultrasonics, 2020, 101: 106001.
15.	Omura M, Yoshida K, Akita S, et al. Verification of echo amplitude envelope analysis method in skin tissues for quantitative follow-up of healing ulcers. Jpn J Appl Phys, 2018, 57(7S1): 07LF15.
16.	Roy-Cardinal M H, Destrempes F, Soulez G, et al. Assessment of carotid artery plaque components with machine learning classification using homodyned-K parametric maps and elastograms. IEEE Trans Ultrason Ferroelectr Freq Control, 2018, 66(3): 493-504.
17.	Hruska D P, Oelze M L. Improved parameter estimates based on the homodyned K distribution. IEEE Trans Ultrason Ferroelectr Freq Control, 2009, 56(11): 2471-2481.
18.	Destrempes F, Porée J, Cloutier G. Estimation method of the homodyned K-distribution based on the mean intensity and two log-moments. SIAM J Imaging Sci, 2013, 6(3): 1499-1530.
19.	Wang C Y, Geng X, Yeh T S, et al. Monitoring radiofrequency ablation with ultrasound Nakagami imaging. Med Phys, 2013, 40(7): 072901.
20.	Jakeman E. On the statistics of K-distributed noise. J Phys A-Math Gen, 1980, 13(1): 31-48.
21.	吴水才, 王月, 周著黄, 等. 基于超声组织定征技术的肿瘤热疗无创监测方法研究进展. 北京工业大学学报, 2017, 43(10): 1488-1496.
22.	Tsui P H, Tsai Y W. Artifact reduction of ultrasound Nakagami imaging by combining multifocus image reconstruction and the noise-assisted correlation algorithm. Ultrasonic imaging, 2015, 37(1): 53-69.
23.	Zhou Z, Wang Y, Song S, et al. Monitoring microwave ablation using ultrasound echo decorrelation imaging: an ex vivo study. Sensors, 2019, 19(4): 977.
24.	Pohlman R M, Varghese T, Jiang J, et al. Comparison of displacement tracking algorithms for in vivo electrode displacement elastography. Ultrasound Med Biol, 2019, 45(1): 218-232.
25.	Ingle A N, Varghese T. A kernel smoothing algorithm for ablation visualization in ultrasound elastography. Ultrasonics, 2019, 96: 267-275.
26.	Su L, Tian W, Xu M, et al. Performance of shear wave elastography in delineating the radiofrequency ablation boundary: an in vivo experiment. Ultrasound Med Biol, 2019, 45(5): 1324-1330.
27.	Zeng X, Zhang Y, Li Z, et al. Locations of optimally matched Gabor atoms from ultrasound RF echoes for inter-scatterer spacing estimation. Comput Meth Prog Bio, 2020, 184: 105281.
28.	Zhang S, Xu R, Shang S, et al. In vivo monitoring of microwave ablation in a porcine model using ultrasonic differential attenuation coefficient intercept imaging. Int J Hyperther, 2018, 34(8): 1157-1170.
29.	Zhang S, Wu S, Shang S, et al. Detection and monitoring of thermal lesions induced by microwave ablation using ultrasound imaging and convolutional neural networks. IEEE J Biomed Health, 2020, 24(4): 965-973.

1. An C, Cheng Z, Yu X, et al. Ultrasound-guided percutaneous microwave ablation of hepatocellular carcinoma in challenging locations: oncologic outcomes and advanced assistive technology. Int J Hyperther, 2020, 37(1): 89-100.
2. Izzo F, Granata V, Grassi R, et al. Radiofrequency ablation and microwave ablation in liver tumors: an update. Oncologist, 2019, 24(10): 1-16.
3. Perrodin S, Lachenmayer A, Maurer M, et al. Percutaneous stereotactic image-guided microwave ablation for malignant liver lesions. Sci Rep-UK, 2019, 9(1): 1-8.
4. Simon C J, Dupuy D E, Mayo-Smith W W. Microwave ablation: principles and applications. Radiographics, 2005, 25(S1): S69-S83.
5. 高钦宗, 王志伟, 金征宇. 肝癌热消融治疗后影像学评价的进展. 医学研究杂志, 2018, 47(9): 10-14.
6. 欧阳亚丽, 周著黄, 吴水才, 等. 超声零差K模型仿真研究. 北京生物医学工程, 2019, 38(2): 13-19.
7. Oelze M L, Mamou J. Review of quantitative ultrasound: envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control, 2016, 63(2): 336-351.
8. Cristea A, Collier N, Franceschini E, et al. Quantitative assessment of media concentration using the Homodyned K distribution. Ultrasonics, 2020, 101: 105986.
9. Zhou Z, Wu S, Wang C Y, et al. Monitoring radiofrequency ablation using real-time ultrasound Nakagami imaging combined with frequency and temporal compounding techniques. PLoS ONE, 2015, 10(2): e0118030.
10. 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. IEEE T Med Imaging, 2018, 37(7): 1701-1710.
11. Destrempes F, Cloutier G. A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope. Ultrasound Med Biol, 2010, 36(7): 1037-1051.
12. Zhou Z, Zhang Q, Wu W, et al. Hepatic steatosis assessment using ultrasound homodyned-K parametric imaging: the effects of estimators. Quant Imaging Med Surg, 2019, 9(12): 1932-1947.
13. Byra M, Nowicki A, Wróblewska-Piotrzkowska H, et al. Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters. Med Phys, 2016, 43(10): 5561-5569.
14. Zhou Z, Fang J, Cristea A, et al. Value of homodyned K distribution in ultrasound parametric imaging of hepatic steatosis: an animal study. Ultrasonics, 2020, 101: 106001.
15. Omura M, Yoshida K, Akita S, et al. Verification of echo amplitude envelope analysis method in skin tissues for quantitative follow-up of healing ulcers. Jpn J Appl Phys, 2018, 57(7S1): 07LF15.
16. Roy-Cardinal M H, Destrempes F, Soulez G, et al. Assessment of carotid artery plaque components with machine learning classification using homodyned-K parametric maps and elastograms. IEEE Trans Ultrason Ferroelectr Freq Control, 2018, 66(3): 493-504.
17. Hruska D P, Oelze M L. Improved parameter estimates based on the homodyned K distribution. IEEE Trans Ultrason Ferroelectr Freq Control, 2009, 56(11): 2471-2481.
18. Destrempes F, Porée J, Cloutier G. Estimation method of the homodyned K-distribution based on the mean intensity and two log-moments. SIAM J Imaging Sci, 2013, 6(3): 1499-1530.
19. Wang C Y, Geng X, Yeh T S, et al. Monitoring radiofrequency ablation with ultrasound Nakagami imaging. Med Phys, 2013, 40(7): 072901.
20. Jakeman E. On the statistics of K-distributed noise. J Phys A-Math Gen, 1980, 13(1): 31-48.
21. 吴水才, 王月, 周著黄, 等. 基于超声组织定征技术的肿瘤热疗无创监测方法研究进展. 北京工业大学学报, 2017, 43(10): 1488-1496.
22. Tsui P H, Tsai Y W. Artifact reduction of ultrasound Nakagami imaging by combining multifocus image reconstruction and the noise-assisted correlation algorithm. Ultrasonic imaging, 2015, 37(1): 53-69.
23. Zhou Z, Wang Y, Song S, et al. Monitoring microwave ablation using ultrasound echo decorrelation imaging: an ex vivo study. Sensors, 2019, 19(4): 977.
24. Pohlman R M, Varghese T, Jiang J, et al. Comparison of displacement tracking algorithms for in vivo electrode displacement elastography. Ultrasound Med Biol, 2019, 45(1): 218-232.
25. Ingle A N, Varghese T. A kernel smoothing algorithm for ablation visualization in ultrasound elastography. Ultrasonics, 2019, 96: 267-275.
26. Su L, Tian W, Xu M, et al. Performance of shear wave elastography in delineating the radiofrequency ablation boundary: an in vivo experiment. Ultrasound Med Biol, 2019, 45(5): 1324-1330.
27. Zeng X, Zhang Y, Li Z, et al. Locations of optimally matched Gabor atoms from ultrasound RF echoes for inter-scatterer spacing estimation. Comput Meth Prog Bio, 2020, 184: 105281.
28. Zhang S, Xu R, Shang S, et al. In vivo monitoring of microwave ablation in a porcine model using ultrasonic differential attenuation coefficient intercept imaging. Int J Hyperther, 2018, 34(8): 1157-1170.
29. Zhang S, Wu S, Shang S, et al. Detection and monitoring of thermal lesions induced by microwave ablation using ultrasound imaging and convolutional neural networks. IEEE J Biomed Health, 2020, 24(4): 965-973.

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