Heart Motion Estimation Using a Deformable Model and Multislice Computerized Tomography Images

Document Type : Original Article (s)

Authors

1 MSc Student, Department of Biomedical Engineering, School of Medicine AND Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran

2 Assistant Professor, Department of Biomedical Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

3 Associate Professor, Department of Cardiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

4 Assistant Professor, Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: Cardiovascular diseases are the main cause of dead in the world. More than 17 million persons die because of heart abnormalities annually. Using noninvasive or low-risk methods instead of invasive and high-risks is necessary. As heart motion is affected by vascular occlusion, in this study heart dense motion field was extracted using a deformable model and multislice computerized tomography (MCT) images to distinguish between heart motion abnormal and normal regions.Methods: MATLAB software was used for simulations and the applied deformable model was active mesh model. The MCT images were turned to short axis images and then segmented. Lastly, the dense motion field of heart was extracted and shown in the standard Bull’s eye form.Findings: The accuracy and sensitivity of extracted dense motion field results, shown in the Bull’s eye form, in comparison with cardiac calcium score results were 70, 71 percent, respectively.Conclusion: Results show that the heart dense motion field extracted using active mesh model is a consistence and promising feature to help diagnosing of heart diseases.

Keywords

References

  1. Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB, et al. Heart disease and stroke statistics--2012 update: a report from the American Heart Association. Circulation 2012; 125(1): e2-e220.
  2. Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation 2002; 105(4): 539-42.
  3. Wang H, Amini AA. Cardiac motion and deformation recovery from MRI: a review. IEEE Trans Med Imaging 2012; 31(2): 487-503.
  4. Sampath S, Prince JL. Automatic 3D tracking of cardiac material markers using slice-following and harmonic-phase MRI. Magn Reson Imaging 2007; 25(2): 197-208.
  5. Bergvall E, Hedstrom E, Bloch KM, Arheden H, Sparr G. Spline-based cardiac motion tracking using velocity-encoded magnetic resonance imaging. IEEE Trans Med Imaging 2008; 27(8): 1045-53.
  6. Osman NF, McVeigh ER, Prince JL. Imaging heart motion using harmonic phase MRI. IEEE Trans Med Imaging 2000; 19(3): 186-202.
  7. Chen T, Wang X, Chung S, Metaxas D, Axel L. Automated 3D motion tracking using Gabor filter bank, robust point matching, and deformable models. IEEE Trans Med Imaging 2010; 29(1): 1-11.
  8. Xu C, Pilla JJ, Isaac G, Gorman JH, III, Blom AS, Gorman RC, et al. Deformation analysis of 3D tagged cardiac images using an optical flow method. J Cardiovasc Magn Reson 2010; 12: 19.
  9. Abd-Elmoniem KZ, Sampath S, Osman NF, Prince JL. Real-time monitoring of cardiac regional function using fastHARP MRI and region-of-interest reconstruction. IEEE Trans Biomed Eng 2007; 54(9): 1650-6.
  10. Makela T, Clarysse P, Sipila O, Pauna N, Pham QC, Katila T, et al. A review of cardiac image registration methods. IEEE Trans Med Imaging 2002; 21(9): 1011-21.
  11. van DP. Direct cardiac NMR imaging of heart wall and blood flow velocity. J Comput Assist Tomogr 1984; 8(3): 429-36.
  12. Nayler GL, Firmin DN, Longmore DB. Blood flow imaging by cine magnetic resonance. J Comput Assist Tomogr 1986; 10(5): 715-22.
  13. Kermani S. Exteraction of 3D heart motion field using acive mesh model and MRI images [PhD Thesis]. Tehran, Iran: Department of Biomedical Engineering, AmirKabir University of Technology; 2008.
  14. Kermani S, Moradi M, Abrishami-Moghadam H, Saneei H. A new approach for quantification of 3D cardiac wall motion tracking using active mesh. Proceedings of the 14th Iranian Conference on Biomedical Engineering; 2008 Feb 13-14; Tehran, Iran.
  15. Kermani S, Moradi M, Abrishami-Moghaddam H, Saneei H, Marashi-Shoshtari M. 3D point wise tracking of the left ventricle over cardiac image sequences using active Mesh and physical models. Journal of Applied Sciences 2008; 8(24): 4500-11.
  16. McInerney T, Terzopoulos D. Deformable models in medical image analysis: a survey. Med Image Anal 1996; 1(2): 91-108.
  17. Mosayebi P, Abrishami-Moghaddam H, Giti M. A fully 3D active mesh model for motion tracking in cardiac MRI. Proceedings of the 9th MICCAI Conference; 2006 Oct 1-6; Copenhagen, Denmark.
  18. Montagnat J, Delingette H. 4D deformable models with temporal constraints: application to 4D cardiac image segmentation. Med Image Anal 2005; 9(1): 87-100.
  19. Mosayebi P, Abrishami-Moghadam H, Giti M. A fully 3D active mesh model for motion tracking in cardiac MRI. Proceedings of the 14th Iranian Conference on Biomedical Engineering; 2008 Feb 13-14; Tehran, Iran.
  20. Mosayebi P, Jamali Dinan F, Abirishami-Moghaddam H, Giti M. Tracking of cardiac motion in 3D MRI sequences using active mesh model. Proceedings of 13th Iranian Conference on Biomedical Engineering; 2007 Feb 21-22; Tehran, Iran.
  21. Lautissier J, Legrand L, Lalande A, Walker P, Brunotte F. Object tracking in medical imaging using a 2D active mesh system. Proceedings of the 25th Annual International Conference of the IEEE; 2003 Sep 17-21; Cancun, Mexico.
  22. Kermani S, Moradi MH, Abrishami-Moghaddam H, Saneei H, Marashi MJ, Shahbazi-Gahrouei D. Quantitative analysis of left ventricular performance from sequences of cardiac magnetic resonance imaging using active mesh model. Comput Med Imaging Graph 2009; 33(3): 222-34.
  23. Fischbach R, Juergens KU, Ozgun M, Maintz D, Grude M, Seifarth H, et al. Assessment of regional left ventricular function with multidetector-row computed tomography versus magnetic resonance imaging. Eur Radiol 2007; 17(4): 1009-17.
  24. Palazzuoli A, Cademartiri F, Geleijnse ML, Meijboom B, Pugliese F, Soliman O, et al. Left ventricular remodelling and systolic function measurement with 64 multi-slice computed tomography versus second harmonic echocardiography in patients with coronary artery disease: a double blind study. Eur J Radiol 2010; 73(1): 82-8.
  25. Ionasec RI, Voigt I, Georgescu B, Wang Y, Houle H, Vega-Higuera F, et al. Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE. IEEE Trans Med Imaging 2010; 29(9): 1636-51.
  26. Wesarg S. AHA conform analysis of myocardial function using and extending the toolkits ITK and VTK. International Congress Series 2005; 1281: 44-9.
  27. Silva S, Santos BS, Madeira J. Exploring different parameters to assess left ventricle global and regional functional analysis from coronary CT angiography. Computer Graphics Forum 2012; 31(1): 146-59.
  28. Heiberg E, Wigström L, Carlsson M, Bolger AF, Karlsson M. Time resolved three-dimensional automated segmentation of the left ventricle. Proceedings of Computers in Cardiology 2005; 2005 Sep 25-28; Lyon, France.
  29. Bistoquet A, Oshinski J, Skrinjar O. Myocardial deformation recovery from cine MRI using a nearly incompressible biventricular model. Med Image Anal 2008; 12(1): 69-85.
Journal of Isfahan Medical School
Volume 31, Issue 234 - Serial Number 234
March and April 2013
Pages 521-531

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History

  • Receive Date: 01 May 2013
  • Revise Date: 01 November 2024
  • Accept Date: 06 April 2022

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