A review on motion tracking methods for left myocardium based on cardiac cine magnetic resonance image_Journal of Biomedical Engineering

Authors：

LIN Jiansheng ,  WANG Lijia

University of Shanghai for Science and Technology, School of Medical Instrument and Food Engineering, Shanghai 200093, P.R.China;

Corresponding author：

Keywords：

cardiac cine magnetic resonance image; left myocardium; motion tracking

DOI：

10.7507/1001-5515.201904007

Video：

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Due to the high spatiotemporal resolution , cardiac cine magnetic resonance imaging (CCMRI) has been widely used to evaluate the cardiac function of cardiovascular diseases such as myocardial ischemia and so on. Segmentation-based motion tracking of left myocardium is very important for comprehensive evaluation of cardiac function in the diagnosis and treatment of cardiovascular diseases. However, it is a challenge to track motion of left myocardium, which is homogeneous and cannot provide effective motion information. In this paper, CCMRI imaging techniques for myocardial motion tracking are introduced firstly. Then approaches for motion tracking of left myocardium based on CCMRI image are described in details and are summarized and prospected at the end, which not only helps beginners to have a quick and comprehensive understanding on this topic, but also provides theoretical reference to related researchers for further optimization of approaches for motion tracking of left myocardium. From the current study, motion tracking approaches for left myocardium based on CCMRI image make comprehensive use of the spatiotemporal motion characteristics of CCMRI image, the motion and structures of myocardium of left ventricle and so on, which can make up for the shortcomings of sparse motion information of CCMRI image. However, it still needs improved constraint framework, verification methods and so on.

Citation： LIN Jiansheng, WANG Lijia. A review on motion tracking methods for left myocardium based on cardiac cine magnetic resonance image. Journal of Biomedical Engineering, 2020, 37(3): 549-556. doi: 10.7507/1001-5515.201904007 Copy

1.	Roth G A, Johnson C, Abajobir A, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol, 2017, 70(1): 1-25.
2.	马丽媛, 吴亚哲, 王文, 等. 《中国心血管病报告 2017》要点解读. 中国心血管杂志, 2018, 23(1): 3-6.
3.	Yan P, Sinusas A, Duncan J S. Boundary element method-based regularization for recovering of LV deformation. Med Image Anal, 2007, 11(6): 540-554.
4.	Yousefi-Banaem H, Kermani S, Asiaei S, et al. Prediction of myocardial infarction by assessing regional cardiac wall in CMR images through active mesh modeling. Comput Biol Med, 2017, 80: 56-64.
5.	Morais P, Queirós S, Ferreira A, et al. Dense motion field estimation from myocardial boundary displacements. International Journal for Numerical Methods in Biomedical Engineering, 2016, 32(9): e02758.
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7.	Wu J, Ruan S, Mazur T R, et al. Heart motion tracking on cine MRI based on a deep boltzmann machine-driven level set method//2018 IEEE 15th International Symposium on Biomedical Imaging. Washington: IEEE, 2018: 1153-1156.
8.	Wu Jian, Mazur T R, Ruan Su, et al. A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images. Medical Image Analysis, 2018, 47: 68-80.
9.	Marchesseau S, Duchateau N, Delingette H. Segmentation and registration coupling from short-axis cine MRI: application to infarct diagnosis//International Workshop on Statistical Atlases and Computational Models of the Heart, Athens: Springer, 2017: 48-56.
10.	Puyol-Antón E, Ruijsink B, Bai W, et al. Fully automated myocardial strain estimation from cine MRI using convolutional neural networks//2018 IEEE 15th International Symposium on Biomedical Imaging. Washington: IEEE, 2018: 1139-1143.
11.	Yousefi-Banaem H, Kermani S, Sanei H, et al. Detecting infarct region in cardiac magnetic resonance images through weighted normalized mutual information. Iran J Radiol, 2017, 14(3): 10-17.
12.	Yousefi-Banaem H, Kermani S, Sanei H, et al. Extraction of left ventricular wall mechanical indexes using four-dimensional image analysis of MRI based on a nonlinear hyperelastic model//Iranian Congress of Radiology. Iranian Society of Radiology, 2018, 34(4): 100.
13.	Mansi T, Pennec X, Sermesant M, et al. iLogDemons: a demons-based registration algorithm for tracking incompressible elastic biological tissues. International Journal of Computer Vision, 2011, 92(1): 92-111.
14.	Guo W, Yang X, Wu J, et al. Left ventricle motion estimation for cardiac cine MRI using graph matching//2017 IEEE International Conference on Bioinformatics and Biomedicine. Kansas City: IEEE, 2017: 724-727.
15.	Zhang Zhengrui, Yang Xuan, Wu Junhao, et al. Left ventricle motion estimation in cine MRI with multilayer iterative deformable graph matching. IEEE Access, 2019, 7: 34791-34806.
16.	Tuyisenge V, Sarry L, Corpetti T, et al. Estimation of myocardial strain and contraction phase from cine MRI using variational data assimilation. IEEE Transactions on Medical Imaging, 2015, 35(2): 442-455.
17.	Queirós S, Vilaça J L, Morais P, et al. Fast left ventricle tracking using localized anatomical affine optical flow. International Journal for Numerical Methods in Biomedical Engineering, 2017, 33(11): e2871.
18.	Wong K L, Wang L, Zhang H, et al. Physiological fusion of functional and structural images for cardiac deformation recovery. IEEE Transactions on Medical Imaging, 2011, 30(4): 990-1000.
19.	Finsberg H, Xi C, Tan J L, et al. Efficient estimation of personalized biventricular mechanical function employing gradient‐based optimization. International Journal for Numerical Methods in Biomedical Engineering, 2018, 34(7): e2982.
20.	Finsberg H, Balaban G, Ross S, et al. Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model. Journal of Computational Science, 2018, 24: 85-90.
21.	Balaban G, Finsberg H, Odland H H, et al. High‐resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle. International Journal for Numerical Methods in Biomedical Engineering, 2017, 33(11): e2863.
22.	Liew Y M, Khalid A, Tan L K, et al. Spatial cardiac dysfunction assessment via personalized modelling from MRI//2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences, Kuching: IEEE, 2018: 69-74.
23.	Gao B, Liu W, Wang L, et al. Estimation of cardiac motion in cine-MRI sequences by correlation transform optical flow of monogenic features distance. Phys Med Biol, 2016, 61(24): 8640-8663.
24.	Benameur N, Caiani E G, Alessandrini M, et al. Left ventricular MRI wall motion assessment by monogenic signal amplitude image computation. Magn Reson Imaging, 2018, 54: 109-118.
25.	Leong C O, Lim E, Tan L K, et al. Segmentation of left ventricle in late gadolinium enhanced MRI through 2D‐4D registration for infarct localization in 3D patient‐specific left ventricular model. Magn Reson Med, 2019, 81(2): 1385-1398.
26.	Wang J, Li W, Sun J, et al. Improved segmental myocardial strain reproducibility using deformable registration algorithms compared with feature tracking cardiac MRI and speckle tracking echocardiography. J Magn Reson Imaging, 2018, 48(2): 404-414.
27.	Lamacie M M, Houbois C P, Greiser A, et al. Quantification of myocardial deformation by deformable registration–based analysis of cine MRI: validation with tagged CMR. European Radiology, 2019, 29: 3658-3668.
28.	Karthikeyan B, Sonkawade S D, Pokharel S, et al. Tagged cine magnetic resonance imaging to quantify regional mechanical changes after acute myocardial infarction. Magnetic Resonance Imaging, 2019, 66: 208-218.
29.	Mantilla J J, Paredes J L, Bellanger J J, et al. Discriminative dictionary learning for local LV wall motion classification in cardiac MRI. Expert Syst Appl, 2019, 129: 286-295.
30.	Zhang N, Yang G, Gao Z, et al. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI. Radiology, 2019, 291(3): 606-617.

1. Roth G A, Johnson C, Abajobir A, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol, 2017, 70(1): 1-25.
2. 马丽媛, 吴亚哲, 王文, 等. 《中国心血管病报告 2017》要点解读. 中国心血管杂志, 2018, 23(1): 3-6.
3. Yan P, Sinusas A, Duncan J S. Boundary element method-based regularization for recovering of LV deformation. Med Image Anal, 2007, 11(6): 540-554.
4. Yousefi-Banaem H, Kermani S, Asiaei S, et al. Prediction of myocardial infarction by assessing regional cardiac wall in CMR images through active mesh modeling. Comput Biol Med, 2017, 80: 56-64.
5. Morais P, Queirós S, Ferreira A, et al. Dense motion field estimation from myocardial boundary displacements. International Journal for Numerical Methods in Biomedical Engineering, 2016, 32(9): e02758.
6. Wael M, Ibrahim E S H, Fahmy A S. Detection of cardiac function abnormality from MRI images using normalized wall thickness temporal patterns. International Journal of Biomedical Imaging, 2016, 2016: 4301087.
7. Wu J, Ruan S, Mazur T R, et al. Heart motion tracking on cine MRI based on a deep boltzmann machine-driven level set method//2018 IEEE 15th International Symposium on Biomedical Imaging. Washington: IEEE, 2018: 1153-1156.
8. Wu Jian, Mazur T R, Ruan Su, et al. A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images. Medical Image Analysis, 2018, 47: 68-80.
9. Marchesseau S, Duchateau N, Delingette H. Segmentation and registration coupling from short-axis cine MRI: application to infarct diagnosis//International Workshop on Statistical Atlases and Computational Models of the Heart, Athens: Springer, 2017: 48-56.
10. Puyol-Antón E, Ruijsink B, Bai W, et al. Fully automated myocardial strain estimation from cine MRI using convolutional neural networks//2018 IEEE 15th International Symposium on Biomedical Imaging. Washington: IEEE, 2018: 1139-1143.
11. Yousefi-Banaem H, Kermani S, Sanei H, et al. Detecting infarct region in cardiac magnetic resonance images through weighted normalized mutual information. Iran J Radiol, 2017, 14(3): 10-17.
12. Yousefi-Banaem H, Kermani S, Sanei H, et al. Extraction of left ventricular wall mechanical indexes using four-dimensional image analysis of MRI based on a nonlinear hyperelastic model//Iranian Congress of Radiology. Iranian Society of Radiology, 2018, 34(4): 100.
13. Mansi T, Pennec X, Sermesant M, et al. iLogDemons: a demons-based registration algorithm for tracking incompressible elastic biological tissues. International Journal of Computer Vision, 2011, 92(1): 92-111.
14. Guo W, Yang X, Wu J, et al. Left ventricle motion estimation for cardiac cine MRI using graph matching//2017 IEEE International Conference on Bioinformatics and Biomedicine. Kansas City: IEEE, 2017: 724-727.
15. Zhang Zhengrui, Yang Xuan, Wu Junhao, et al. Left ventricle motion estimation in cine MRI with multilayer iterative deformable graph matching. IEEE Access, 2019, 7: 34791-34806.
16. Tuyisenge V, Sarry L, Corpetti T, et al. Estimation of myocardial strain and contraction phase from cine MRI using variational data assimilation. IEEE Transactions on Medical Imaging, 2015, 35(2): 442-455.
17. Queirós S, Vilaça J L, Morais P, et al. Fast left ventricle tracking using localized anatomical affine optical flow. International Journal for Numerical Methods in Biomedical Engineering, 2017, 33(11): e2871.
18. Wong K L, Wang L, Zhang H, et al. Physiological fusion of functional and structural images for cardiac deformation recovery. IEEE Transactions on Medical Imaging, 2011, 30(4): 990-1000.
19. Finsberg H, Xi C, Tan J L, et al. Efficient estimation of personalized biventricular mechanical function employing gradient‐based optimization. International Journal for Numerical Methods in Biomedical Engineering, 2018, 34(7): e2982.
20. Finsberg H, Balaban G, Ross S, et al. Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model. Journal of Computational Science, 2018, 24: 85-90.
21. Balaban G, Finsberg H, Odland H H, et al. High‐resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle. International Journal for Numerical Methods in Biomedical Engineering, 2017, 33(11): e2863.
22. Liew Y M, Khalid A, Tan L K, et al. Spatial cardiac dysfunction assessment via personalized modelling from MRI//2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences, Kuching: IEEE, 2018: 69-74.
23. Gao B, Liu W, Wang L, et al. Estimation of cardiac motion in cine-MRI sequences by correlation transform optical flow of monogenic features distance. Phys Med Biol, 2016, 61(24): 8640-8663.
24. Benameur N, Caiani E G, Alessandrini M, et al. Left ventricular MRI wall motion assessment by monogenic signal amplitude image computation. Magn Reson Imaging, 2018, 54: 109-118.
25. Leong C O, Lim E, Tan L K, et al. Segmentation of left ventricle in late gadolinium enhanced MRI through 2D‐4D registration for infarct localization in 3D patient‐specific left ventricular model. Magn Reson Med, 2019, 81(2): 1385-1398.
26. Wang J, Li W, Sun J, et al. Improved segmental myocardial strain reproducibility using deformable registration algorithms compared with feature tracking cardiac MRI and speckle tracking echocardiography. J Magn Reson Imaging, 2018, 48(2): 404-414.
27. Lamacie M M, Houbois C P, Greiser A, et al. Quantification of myocardial deformation by deformable registration–based analysis of cine MRI: validation with tagged CMR. European Radiology, 2019, 29: 3658-3668.
28. Karthikeyan B, Sonkawade S D, Pokharel S, et al. Tagged cine magnetic resonance imaging to quantify regional mechanical changes after acute myocardial infarction. Magnetic Resonance Imaging, 2019, 66: 208-218.
29. Mantilla J J, Paredes J L, Bellanger J J, et al. Discriminative dictionary learning for local LV wall motion classification in cardiac MRI. Expert Syst Appl, 2019, 129: 286-295.
30. Zhang N, Yang G, Gao Z, et al. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI. Radiology, 2019, 291(3): 606-617.

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