Global Tracking of Myocardial Motion in Ultrasound Sequence Images: A Feasibility Study

Yinong Wang; Xiaomin Liu; Xiangfen Song; Qing Wang; Qianjin Feng; Wufan Chen; Yinong Wang; Xiaomin Liu; Xiangfen Song; Qing Wang; Qianjin Feng; Wufan Chen

doi:10.3934/mbe.2020026

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 1: 478-493. doi: 10.3934/mbe.2020026

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Global Tracking of Myocardial Motion in Ultrasound Sequence Images: A Feasibility Study

1.
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
2.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China

Received: 30 April 2019 Accepted: 29 August 2019 Published: 17 October 2019

The assessment of myocardial motion plays a promising role in the evaluation of cardiac function. This study aims to propose a novel framework of global estimation of the myocardial motion using radio-frequency (RF) data. The framework consists of B-mode image reconstruction, displacement estimation, myocardium extraction, and image fusion. The RF data of murine heart in parasternal long-axis (PLAX) view were collected for B-mode image reconstruction and displacement estimation. The vectorized normalized cross-correlation (VNCC) approach was proposed to globally estimate the displacements of the RF frames, while a sum-table based normalized cross-correlation (STNCC) was performed as reference algorithm. The bimodal fusion images were obtained to visualize the motion and anatomical structure of myocardium by an improved fast mapping algorithm (IFMA). In comparison with STNCC, the computation time of displacement using VNCC reduced by approximate 10s. The myocardial motions of anterior wall and posterior wall during one cardiac cycle were similarly tracked by VNCC as that of STNCC. The averaged absolute error in displacement between the two methods ranges from 1 to 3μm. The obtained myocardial elastographic images using VNCC intuitively present the morphological and mechanical changes during the contraction period of left ventricle. The results demonstrate that the proposed framework is an efficient tool for the estimation of myocardial motion reflecting cardiac systolic function. This approach has potentials to provide visualized information of myocardium for diagnosis and prognosis of cardiovascular diseases (CVDs).
- ultrasound elastography,
- myocardial motion,
- vectorized normalized cross-correlation,
- myocardium segmentation
Citation: Yinong Wang, Xiaomin Liu, Xiangfen Song, Qing Wang, Qianjin Feng, Wufan Chen. Global Tracking of Myocardial Motion in Ultrasound Sequence Images: A Feasibility Study[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 478-493. doi: 10.3934/mbe.2020026

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Abstract

The assessment of myocardial motion plays a promising role in the evaluation of cardiac function. This study aims to propose a novel framework of global estimation of the myocardial motion using radio-frequency (RF) data. The framework consists of B-mode image reconstruction, displacement estimation, myocardium extraction, and image fusion. The RF data of murine heart in parasternal long-axis (PLAX) view were collected for B-mode image reconstruction and displacement estimation. The vectorized normalized cross-correlation (VNCC) approach was proposed to globally estimate the displacements of the RF frames, while a sum-table based normalized cross-correlation (STNCC) was performed as reference algorithm. The bimodal fusion images were obtained to visualize the motion and anatomical structure of myocardium by an improved fast mapping algorithm (IFMA). In comparison with STNCC, the computation time of displacement using VNCC reduced by approximate 10s. The myocardial motions of anterior wall and posterior wall during one cardiac cycle were similarly tracked by VNCC as that of STNCC. The averaged absolute error in displacement between the two methods ranges from 1 to 3μm. The obtained myocardial elastographic images using VNCC intuitively present the morphological and mechanical changes during the contraction period of left ventricle. The results demonstrate that the proposed framework is an efficient tool for the estimation of myocardial motion reflecting cardiac systolic function. This approach has potentials to provide visualized information of myocardium for diagnosis and prognosis of cardiovascular diseases (CVDs).

References

[1]	B. Bijnens, M. Cikes, C. Butakoff, et al., Myocardial motion and deformation: What does it tell us and how does it relate to function?, Fetal Diagn. Ther., 32 (2012), 5-16.
[2]	N. Mangner, K. Scheuermann, E. Winzer, et al., Childhood obesity: Impact on cardiac geometry and function, JACC Cardiovasc. Imaging, 7 (2014), 1198-1205.
[3]	T. Asanuma and S. Nakatani, Myocardial ischaemia and post-systolic shortening, Heart, 101 (2015), 509-516.
[4]	P. Brainin, K. G. Skaarup, A. Z. Iversen, et al., Post-systolic shortening predicts heart failure following acute coronary syndrome, Int. J. Cardiol., 276 (2019), 191-197. doi: 10.1016/j.ijcard.2018.11.106
[5]	M. S. Huang, W. H. Lee, H. R. Tsai, et al., Value of layer-specific strain distribution patterns in hypertrophied myocardium from different etiologies, Int. J. Cardiol., 281 (2019), 69-75.
[6]	K. Shiino, A. Yamada, G. M. Scalia, et al., Early changes of myocardial function after transcatheter aortic valve implantation using multilayer strain speckle tracking echocardiography, Am. J. Cardiol., 123 (2019), 956-960.
[7]	W. N. McDicken, G. R. Sutherland, C. M. Moran, et al., Colour Doppler velocity imaging of the myocardium, Ultrasound Med. Biol., 18 (1992), 651-654. doi: 10.1016/0301-5629(92)90080-T
[8]	J. Ophir, I. Cespedes, H. Ponnekanti, et al., Elastography: A quantitative method for imaging the elasticity of biological tissues, Ultrason. Imaging, 13 (1991), 111-134.
[9]	E. E. Konofagou, J. D'Hooge and J. Ophir, Myocardial elastography-a feasibility study in vivo, Ultrasound Med. Biol., 28 (2002), 475-482.
[10]	M. Lu, Y. Tang, R. Sun, et al., A real time displacement estimation algorithm for ultrasound elastography, Comput. Ind., 69 (2015), 61-71.
[11]	F. Viola and W. F. Walker, A comparison of the performance of time-delay estimators in medical ultrasound, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 50 (2003), 392-401.
[12]	S. Langeland, J. D'Hooge, H. Torp, et al., Comparison of time-domain displacement estimators for two-dimensional RF tracking, Ultrasound Med. Biol., 29 (2003), 1177-1186.
[13]	W. N. Lee, J. Provost, K. Fujikura, et al., In vivo study of myocardial elastography under graded ischemia conditions, Phys. Med. Biol., 56 (2011), 1155-1172.
[14]	E. Brusseau, V. Detti, A. Coulon, et al., In Vivo response to compression of 35 breast lesions observed with a two-dimensional locally regularized strain estimation method, Ultrasound Med. Biol., 40 (2014), 300-312.
[15]	J. Luo and E. Konofagou, A fast normalized cross-correlation calculation method for motion estimation, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 57 (2010), 1347-1357.
[16]	J. Luo, K. Fujikura, S. Homma, et al., Myocardial elastography at both high temporal and spatial resolution for the detection of infarcts, Ultrasound Med. Biol., 33 (2007), 1206-1223.
[17]	Q. He, L. Tong, L. Huang, et al., Performance optimization of lateral displacement estimation with spatial angular compounding, Ultrasonics, 73 (2017), 9-21.
[18]	S. Rezajoo and A. R. Sharafat, Robust estimation of displacement in real-time freehand ultrasound strainimaging, IEEE Trans. Med. Imaging, 37 (2018), 1664-1677.
[19]	L. Gong, D. Li, J. Chen, et al., Assessment of myocardial viability in patients with acute myocardial infarction by two-dimensional speckle tracking echocardiography combined with low-dose dobutamine stress echocardiography, Int. J. Cardiovasc. Imaging, 29 (2013), 1017-1028.
[20]	T. Zakaria, Z. Qin and R. L. Maurice, Optical-flow-based B-mode elastography: Application in the hypertensive rat carotid, IEEE Trans. Med. Imaging, 29 (2010), 570-578.
[21]	M. S. Richards and M. M. Doyley, Non-rigid image registration based strain estimator for intravascular ultrasound elastography, Ultrasound Med. Biol., 39 (2013), 515-533.
[22]	H. Rivaz, E. Boctor, P. Foroughi, R. Zellars, G. Fichtinger, and G. Hager, Ultrasound elastography: A dynamic programming approach, IEEE Trans. Med. Imaging, 27 (2008), 1373-1377.
[23]	L. Chen, G. M. Treece, J. E. Lindop, et al., A quality-guided displacement tracking algorithm for ultrasonic elasticity imaging, Med. Image Anal., 13 (2009), 286-296.
[24]	H. S. Hashemi and H. Rivaz, Global time-delay estimation in ultrasound elastography, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 64 (2017), 1625-1636.
[25]	J. Luo and E. E. Konofagou, High-frame rate, full-view myocardial elastography with automated contour tracking in murine left ventricles in vivo, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 55 (2008), 240-248.
[26]	J. P. Lewis, Fast normalized cross-correlation,1995, Vision Interface, 2010 (2010), 120-123.
[27]	Y. N. Wang, X. F. Song, Z. J. Huang, et al., Myocardial elastogram using a fast mapping algorithm, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, 3236-3239.
[28]	H. Rivaz, E. M. Boctor, M. A. Choti, et al., Real-time regularized ultrasound elastography programming approach, IEEE Trans. Med. Imaging, 30 (2011), 928-945.
[29]	Y. Kuwada and K. Takenaka, Transmural heterogeneity of the left ventricular wall: Subendocardial layer and subepicardial layer, J. Cardiol., 35 (2000), 205-218.
[30]	P. P. Sengupta, J. Korinek, M. Belohlavek, et al., Left ventricular structure and function: Basic science for cardiac imaging, J. Am. Coll. Cardiol., 48 (2006), 1988-2001.
[31]	J. Grondin, A. Costet, E. Bunting, et al., Validation of electromechanical wave imaging in a canine model during pacing and sinus rhythm, Heart Rhythm, 13 (2016), 2221-2227. doi: 10.1016/j.hrthm.2016.08.010
[32]	I. Cespedes, Y. Huang, J. Ophir, et al., Methods for estimation of subsample time delays of digitized echo signals, Ultrason. Imaging, 17 (1995), 142-171.
[33]	F. Kallel and J. Ophir, Three-dimensional tissue motion and its effect on image noise in elastography, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 44 (1997), 1286-1296.
[34]	Q. Huang, J. Lan and X. Li, Robotic Arm Based Automatic Ultrasound Scanning for Three-Dimensional Imaging, IEEE Trans. Ind. Inf., 15 (2019), 1173-1182.
[35]	Q. Huang and J. Lan, Remote control of a robotic prosthesis arm with six-degree-of-freedom for ultrasonic scanning and three-dimensional imaging, Biomed. Signal Process. Control, 54 (2019), 101606.

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