Registration, modal decomposition and analysis of human left ventricles

• Autorzy: Stankiewicz Witold

Identyfikatory

DOI

10.21008/j.0860-6897.2023.2.24

• Języki publikacji: EN

Abstrakty

EN

Cardiovascular diseases, especially myocardial infarction and heart failure, are among the most common causes of death. Proper, timely diagnosis can be a key factor in reducing the mortality of these diseases. In the present paper, statistical data analysis of left ventricle of human heart is presented. Raster DICOM images are processed, segmented and registered, in order to mark the left ventricle on medical images, and then to obtain its geometrical 3D models of constant topology. Registered, geometrical data, obtained for whole cardiac cycle of patients with healthy hearts, hypertrophy and heart failure, is then decomposed using Principal Component Analysis. The obtained modes represent the movement of the ventricle during one heart cycle. The proposed approach allows neglecting unimportant, noisy signal and enables the interpretation of the heart cycle. It is shown that modal decomposition might be used to distinguish the hearts with heart failure and the group containing healthy hearts and the ones with hypertrophy. Being a non-invasive method, this approach enables the diagnosis of various hearts, including prenatal ones.

Słowa kluczowe

EN

principal component analysis; modal decomposition; ventricle; segmentation; registration

PL

analiza głównych składowych; rozkład modalny; komora serca; segmentacja; rejestracja

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2023
• Tom: Vol. 34, nr 2
• Strony: art. no. 2023224
• Opis fizyczny: Bibliogr. 38 poz., il. kolor., rys., wykr.

Twórcy

autor

Stankiewicz Witold

• Poznan University of Technology, Institute of Applied Mechanics, ul. Jana Pawła II 24, 60-965 Poznań, Poland

• https://orcid.org/0000-0002-5159-7295

Bibliografia

• 1. Eurostat; Deaths from cardiovascular diseases; Technical report, European Union, 2018, https://ec.europa.eu/eurostat/statistics-explained/index.php/Cardiovascular_diseases_statistics; (accessed on 20.09.2021)
• 2. J. P. Greenwood, N. Maredia, J. F. Younger, et al.; Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (ce-marc): a prospective trial; Lancet, 2012, 379(9814), 453-60
• 3. F. von Knobelsdorff-Brenkenhoff, J. Schulz-Menger; Role of cardiovascular magnetic resonance in the guidelines of the European society of cardiology; J. Cardiovasc. Magnetic. Reson., 2016, 18(1), 6
• 4. M. A. Viergever, J. B. A. Maintz, S. Klein, K. Murphy, M. Staring, J. P. W. Pluim; A survey of medical image registration - under review; Med. Image Anal., 2016, 33, 140-144
• 5. S.K. Zhou., H. Greenspan, D. Shen; Deep learning for medical image analysis; Academic Press, Elsevier, 2017
• 6. J. Ker, L. Wang, J. Rao, T. Lim; Deep learning applications in medical image analysis; IEEE Access 2017, 6, 9375-9389
• 7. H. Hu, N. Pan, J. Wang, T. Yin, R. Ye; Automatic segmentation of left ventricle from cardiac MRI via deep learning and region constrained dynamic programming; Neurocomputing, 2019, 347, 139-148
• 8. L.K. Tan, R.A. McLaughlin, E. Lim, Y.F. Abdul Aziz, Y.M. Liew; Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression; J. Magn. Reson. Imaging, 2018, 48(1), 140-152
• 9. C.D. Kemp, J.V. Conte; The pathophysiology of heart failure; Cardiovasc. Pathol., 2012, 21(5), 365-371
• 10. Mayo Clinic. Diseases and conditions; https://www.mayoclinic.org/diseases-conditions/; 2021, (accessed on 20.09.2021)
• 11. F. Van de Werf, J. Bax, A. Betriu, et al; Management of acute myocardial infarction in patients presenting with persistent st-segment elevation: the task force on the management of st-segment elevation acute myocardial infarction of the european society of cardiology; Eur. Heart J., 2008, 29(23), 2909-2945
• 12. M. Yildiz, A.A. Oktay, M.H. Stewart, R.V. Milani, H.O. Ventura, C.J. Lavie; Left ventricular hypertrophy and hypertension; Prog. Cardiovasc. Dis., 2020, 63(1), 10-21
• 13. P. Radau, Y. Lu, K. Connelly, G. Paul, A.J. Dick, G.A. Wright; Evaluation framework for algorithms segmenting short axis cardiac MRI; MIDAS J - Cardiac MR Left Ventricle Segmentation Challenge, 2009, 49; Dataset is available at: https://www.cardiacatlas.org/sunnybrook-cardiac-data/
• 14. P. Przybyła, W. Stankiewicz, M. Morzyński, M. Nowak, D. Gaweł, S. Stefaniak, M. Jemielity; Reduced order model of a human left and right ventricle based on POD method. In: Computational Biomechanics for Medicine: From Algorithms to Models and Applications; A. Wittek, G. Joldes, P.M.F. Nielsen, B.J. Doyle, K. Miller, editors; Springer, Cham, 2017, 57-69
• 15. G. Bradski, A. Kaehler; Learning OpenCV: Computer vision with the OpenCV library; O’Reilly Media Inc., 2008
• 16. P. Dierckx; Curve and surface fitting with splines; Oxford University Press: Oxford, 1995
• 17. P. Perona, J. Malik; Scale-space and edge detection using anisotropic diffusion; IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12(7), 629-639
• 18. T. S. Huang, G. J. Yang, G. Y. Tang; A fast two-dimensional median filtering algorithm; IEEE Trans. Acoust., 1979, 27(1), 13-18
• 19. S. Suzuki, K. Abe; Topological structural analysis of digitized binary images by border following; Comput. Vis. Graph. Image Process., 1985, 30(1), 32-46
• 20. E. Jones, T. Oliphant, P. Peterson; SciPy: Open source scientific tools for Python; https://www.scipy.org; 2021
• 21. M. Rychlik, W. Stankiewicz, M. Morzyński; Numerical analysis of geometrical features of 3D biological objects, for three-dimensional biometric and anthropometric database; In: UAHCI 2011. Lecture Notes in Computer Science; Stephanidis C., editor; Springer, Berlin-Heidelberg, 2011, 6766, 108-117
• 22. N. Aubry, R. Guyonnet, R. Lima; Spatiotemporal analysis of complex signals: Theory and applications; J. Stat. Phys., 1991, 64(2/3), 683-739
• 23. G. Berkooz, P. Holmes, Lumley JL; The proper orthogonal decomposition in the analysis of turbulent flows; Ann. Rev. Fluid Mech., 1993, 25(1), 539-575
• 24. L. Sirovich; Turbulence and the dynamics of coherent structures; Q. Appl. Math., 1987, 45, 561-571
• 25. H. Lu, K.N. Plataniotis, A.N. Venetsanopoulos; MPCA: Multilinear principal component analysis of tensor objects; IEEE Trans. Neural. Netw., 2008, 19(1), 18-39
• 26. H. Zou, T. Hastie, R. Tibshirani; Sparse principal component analysis; J. Comput. Graph. Stat., 2006, 15(2), 265-286
• 27. H. Hoffmann; Kernel PCA for novelty detection; Pattern Recognition 2007, 40, 863-874
• 28. H. Yu, J. Yang; A direct LDA algorithm for high-dimensional data - with application to face recognition; Pattern recognit., 2001, 34(10), 2067-2070
• 29. A. Hyvärinen, O. Erkki; Independent component analysis: algorithms and applications; Neural Netw., 2000, 13(4-5), 411-430
• 30. S. Nakatani; Left ventricular rotation and twist: why should we learn?; J Cardiovasc Ultrasound, 2011, 19(1), 1-6
• 31. P. Ghorbanian, A. Ghaffari, A. Jalali, C. Nataraj; Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier; In: Computing in Cardiology, IEEE, 2010, 669-672
• 32. R. J. Martis, U. R. Acharya, K. M. Mandana, A. K. Ray, C. Chakraborty; Application of principal component analysis to ECG signals for automated diagnosis of cardiac health; Expert Syst. Appl., 2012, 39(14), 11792-11800
• 33. A. K. Gárate-Escamila, A. H. El Hassani, E. Andrès; Classification models for heart disease prediction using feature selection and PCA; Inform. Med. Unlocked, 2020, 19, 100330
• 34. D. Perperidis, R. Mohiaddin, P. Edwards, D. Rueckert; Segmentation of cardiac MR and CT image sequences using model-based registration of a 4D statistical model; Medical imaging 2007: Image Processing, 2007, 6512:65121D
• 35. J. Wu, Y. Wang, M. A. Simon, J. C. Brigham; A new approach to kinematic feature extraction from the human right ventricle for classification of hypertension: a feasibility study; Physics in Medicine and Biology, 2012, 57(23), 7905-7922
• 36. W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery; Numerical Recipes in C: The Art of Scientific Computing, second edition; Cambridge University Press, Cambridge, MA, 1992
• 37. D. Mele, M. Nardozza, R. Ferrari; Left ventricular ejection fraction and heart failure: an indissoluble marriage?; Eur. J. Heart Fail., 2018, 20, 427-430
• 38. C. Zhang, T. Chen; Efficient feature extraction for 2D/3D objects in mesh representation; In: Proceedings of 2001 International Conference on Image Processing (Cat. No. 01CH37205), vol. 3; Institute of Electrical and Electronics Engineers, 2001, 935-938

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).