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Patient-Specific Mapping between Myocardium and Coronary Arteries using Myocardial Thickness Variation

  • Dongjin Han (Department of Fire Safety, Kyung-Il university)
  • Received : 2024.05.06
  • Accepted : 2024.05.20
  • Published : 2024.06.30

Abstract

For precise cardiac diagnostics and treatment, we introduce a novel method for patient-specific mapping between myocardial and coronary anatomy, leveraging local variations in myocardial thickness. This complex system integrates and automates multiple sophisticated components, including left ventricle segmentation, myocardium segmentation, long-axis estimation, coronary artery tracking, and advanced geodesic Voronoi distance mapping. It meticulously accounts for variations in myocardial thickness and precisely delineates the boundaries between coronary territories according to the conventional 17-segment myocardial model. Each phase of the system provides a step-by-step approach to automate coronary artery mapping onto the myocardium. This innovative method promises to transform cardiac imaging by offering highly precise, automated, and patient-specific analyses, potentially enhancing the accuracy of diagnoses and the effectiveness of therapeutic interventions for various cardiac conditions.

Keywords

References

  1. G. Bastarrika, L. Ramos-Duran, M. A. Rosenblum, D. K. Kang, G. W. Rowe, and U. J. Schoepf, "Adenosine-stress dynamic myocardial CT perfusion imaging: initial clinical experience," Investigative Radiology, vol. 45, no. 6, pp. 306-313, 2010. https://doi.org/10.1097/RLI.0b013e3181dfa2f2
  2. M. D. Cerqueira, N. J. Weissman, V. Dilsizian, A. K. Jacobs, S. Kaul, W. K. Laskey, D. J. Pennell, J. A. Rumberger, T. Ryan, M. S. Verani, 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, vol. 105, no. 4, pp. 539-542, 2002. https://doi.org/10.1161/hc0402.102975
  3. H. Kalbfleisch and W. Hort, "Quantitative study on the size of coronary artery supplying areas postmortem," American Heart Journal, vol. 94, no. 2, pp. 183-188, 1977. https://doi.org/10.1016/S0002-8703(77)80278-0
  4. Y. Koiwa, R. Bahn, and E. Ritman, "Regional myocardial volume perfused by the coronary artery branch: estimation in vivo," Circulation, vol. 74, no. 1, pp. 157-163, 1986. https://doi.org/10.1161/01.CIR.74.1.157
  5. M. Termeer, J. Bescos, M. Breeuwer, A. Vilanova, F. Gerritsen, E. Groller, and E. Nagel, "Patient-specific coronary artery supply territory AHA diagrams," Journal of Cardiovascular Magnetic Resonance, vol. 11, Suppl 1, p. P103, 2009.
  6. M. Weininger, U. J. Schoepf, A. Ramachandra, C. Fink, G. W. Rowe, P. Costello, and T. Henzler, "Adenosine-stress dynamic real-time myocardial perfusion CT and adenosine-stress first-pass dual-energy myocardial perfusion CT for the assessment of acute chest pain: initial results," European Journal of Radiology, vol. 81, no. 12, pp. 3703-3710, 2012. https://doi.org/10.1016/j.ejrad.2010.11.022
  7. M. T. Dehkordi, S. Sadri, and A. Doosthoseini, "A review of coronary vessel segmentation algorithms," J Med Signals Sens, vol. 1, no. 1, pp. 49-54, 2011.
  8. M. M. Hadhoud, M. I. Eladawy, A. Farag, F. M. Montevecchi, and U. Morbiducci, "Left ventricle segmentation in cardiac MRI images," American Journal of Biomedical Engineering, vol. 2, no. 3, pp. 131-135, 2012. https://doi.org/10.5923/j.ajbe.20120203.07
  9. M. P. Jolly, "Automatic segmentation of the left ventricle in cardiac MR and CT images," International Journal of Computer Vision, vol. 70, no. 2, pp. 151-163, 2006. https://doi.org/10.1007/s11263-006-7936-3
  10. M. R. Kaus, J. von Berg, J. Weese, W. Niessen, and V. Pekar, "Automated segmentation of the left ventricle in cardiac MRI," Med Image Anal, vol. 8, no. 3, pp. 245-254, 2004. https://doi.org/10.1016/j.media.2004.06.015
  11. J. Lessick, Y. Fisher, R. Beyar, S. Sideman, M. L. Marcus, and H. Azhari, "Regional three-dimensional geometry of the normal human left ventricle using cine computed tomography," Ann Biomed Eng, vol. 24, no. 5, pp. 583-594, 1996. https://doi.org/10.1007/BF02684227
  12. D. Lesage, E. D. Angelini, I. Bloch, and G. Funka-Lea, "A review of 3D vessel lumen segmentation techniques: models, features, extraction schemes and algorithms," Med Image Anal, vol. 13, no. 6, pp. 819-845, 2009. https://doi.org/10.1016/j.media.2009.07.011
  13. D. Marin, A. Aquino, M. Emilio Gegundez-Arias, and J. Manuel Bravo, "A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features," IEEE Transactions on Medical Imaging, vol. 30, no. 1, pp. 146-158, 2011. https://doi.org/10.1109/TMI.2010.2064333
  14. C. Kirbas and F. Quek, "A review of vessel extraction techniques and algorithms," ACM Computing Surveys, vol. 36, no. 2, pp. 81-121, 2004. https://doi.org/10.1145/1031120.1031121
  15. Dongjin Han et al., "Automatic coronary artery segmentation using active search for branches and seemingly disconnected vessel segments from coronary CT angiography," PloS One, vol. 118, e0156837, 18 Aug. 2016. https://doi.org/10.1371/journal.pone.0156837.
  16. J. K. Min, J. Leipsic, M. J. Pencina, D. S. Berman, B.-K. Koo, C. van Mieghem, A. Erglis, F. Y. Lin, A. M. Dunning, P. Apruzzese, et al., "Diagnostic accuracy of fractional flow reserve from anatomic CT angiography: fractional flow reserve from CT angiography," JAMA, vol. 308, no. 12, pp. 1237-1245, 2012. https://doi.org/10.1001/2012.jama.11274
  17. C. Petitjean and J. N. Dacher, "A review of segmentation methods in short axis cardiac MR images," Med Image Anal, vol. 15, no. 2, pp. 169-184, 2011.
  18. G. Peyre, M. Pechaud, R. Keriven, and L. Cohen, "Geodesic methods in computer vision and graphics," Foundations and Trends in Computer Graphics and Vision, vol. 5, nos. 3-4, pp. 197-397, 2010.
  19. M. Prasad, A. Ramesh, P. Kavanagh, B. K. Tamarappoo, R. Nakazato, J. Gerlach, V. Cheng, L. E. Thomson, D. S. Berman, G. Germano, and P. J. Slomka, "Quantification of 3D regional myocardial wall thickening from gated magnetic resonance images," J Magn Reson Imaging, vol. 31, no. 2, pp. 317-327, 2010. https://doi.org/10.1002/jmri.22033
  20. Shaaf ZF, Jamil MMA, Ambar R, Alattab AA, Yahya AA, Asiri Y. "Automatic left ventricle segmentation from short-axis cardiac MRI images based on fully convolutional neural network," Diagnostics (Basel), vol. 12, no. 2, p. 414, 2022. https://doi.org/10.3390/diagnostics12020414.