Skip to main content
SpringerLink
Log in

Morphologica l and Functional Modeling of the Heart Valves and Chambers

  • Chapter
  • First Online:
Patient-Specific Modeling in Tomorrow's Medicine

Part of the book series: Studies in Mechanobiology, Tissue Engineering and Biomaterials ((SMTEB,volume 09))

  • 1629 Accesses

Abstract

Personalized cardiac models have become a crucial component of the clinical workflow, especially in the context of complex cardiovascular disorders, such as valvular heart disease. In this chapter we present a comprehensive framework for the patient-specific modeling of the valvular apparatus and heart chambers from multi-modal cardiac images. An integrated model of the four heart valves and chambers is introduced, which captures a large spectrum of morphologic, dynamic and pathologic variations. The patient-specific model parameters are estimated from four-dimensional cardiac images using robust learning-based techniques. These include object localization, rigid and non-rigid motion estimation, and surface boundary estimation from dense 4D data (TEE, CT) as well as regression-based techniques for surface reconstruction from sparse 4D data (MRI). Clinical applications based on the patient-specific modeling approach are proposed for decision support in Transcatheter Aortic Valve Implantation and Percutaneous Pulmonary Valve Implantation while performance evaluation is conducted on a population of 476 patients.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agarwal, A., Triggs, B.: Tracking articulated motion using a mixture of autoregressive models. In: Proceedings of the European Conference Computer Vision, pp. III 54–65 (2004)

    Google Scholar 

  2. Akhtar, M., Tuzcu, E.M., Kapadia, S.R., Svensson, L.G., Greenberg, R.K., Roselli, E.E., Halliburton, S., Kurra, V., Schoenhagen, P., Sola, S.: Aortic root morphology in patients undergoing percutaneous aortic valve replacement: Evidence of aortic root remodeling. J. Thorac. Cardiovasc. Surg. 137, 950–956 (2009)

    Article  Google Scholar 

  3. Bonhoeffer, P., Boudjemline, S.A., Qureshi, Y., Bidois, J.L., Iserin, L., Acar, P., Merckx, J., Kachaner, J., Sidi, D.: Percutaneous insertion of the pulmonary valve. J. Am. Coll. Cardiol. 39, 1664–1669 (2002)

    Article  Google Scholar 

  4. Bonow, R.O., Carabello, B.A., Chatterjee, K., de Leon, A.C.J., Faxon, D.P., Freed, M.D., Gaasch, W.H., Lytle, B.W., Nishimura, R.A., O’Gara, P.T., O’Rourke, R.A., Otto, C.M., Shah, P.M., Shanewise, J.S.: Acc/aha 2006 guidelines for the management of patients with valvular heart disease: a report of the american college of cardiology/american heart association task force on practice guidelines (writing committee to develop guidelines for the management of patients with valvular heart disease). Circulation 114, 84–231 (2006)

    Article  Google Scholar 

  5. Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989)

    Article  MATH  Google Scholar 

  6. Boudjemline, Y., Agnoletti, G., Bonnet, D., Sidi, D., Bonhoeffer, P.: Percutaneous pulmonary valve replacement in a large right ventricular outflow tract: An experimental study. Am. Coll. Cardiol. 43, 1082–1087 (2004)

    Article  Google Scholar 

  7. Calleja, A., Razvan, I., Houle, H., Liu, S., Dickerson, J., Thavendiranathan, P., Sai-Sudhakar, C., Crestanello, J., Ryan, T., Vannan, M.: Automated quantitative modeling of the aortic valve and root in aortic regurgitation using volume 3-d transesophageal echocardiography. In: American College of Cardiology Annual Meeting-ACC 2010, Atlanta, USA (2010)

    Google Scholar 

  8. Carnaghan, H.: Percutaneous pulmonary valve implantation and the future of replacement. Sci. Technol. 20, 319–322 (2006)

    Google Scholar 

  9. Choi, J.H., Georgescu, B., Ionasec, R.I., Raman, S., Hong, G.R., Liu, S., Houle, H., Vannan, M.A.: Novel semi-automatic quantitative assessment of the aortic valve and aortic root from volumetric 3d echocardiography: comparison to volumetric cardiac computed tomography (ct). In: AHA, New Orleans, USA (2008)

    Google Scholar 

  10. Conti, C., Stevanella, M., Maffessanti, F., Trunfio, S., Votta, E., Roghi, A., Parodi, O., Caiani, E., Redaelli, A.: Mitral valve modelling in ischemic patients: finite element analysis from cardiac magnetic resonance imaginge. In: Computing in Cardiology (2010)

    Google Scholar 

  11. De Hart, J., Peters,G., Schreurs, P., Baaijens, F.: A three-dimensional computational analysis of fluid-structure interaction in the aortic valve. J. Biomech. 36, 103–110 (2002)

    Article  Google Scholar 

  12. Ecabert, O., Peters, J., Schramm, H., Lorenz, C., von Berg, J., Walker, M.J., Vembar, M., Olszewski, M.E., Subramanyan, K., Lavi, G., Weese, J.: Automatic model-based segmentation of the heart in CT images. IEEE Trans. Med. Imaging 27, 1189–1201 (2008)

    Article  Google Scholar 

  13. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2000)

    Article  Google Scholar 

  14. Fritz, D., Rinck, D., Dillmann, R., Scheuring, M.: Segmentation of the left and right cardiac ventricle using a combined bi-temporal statistical model. SPIE Med. Imaging 6141, 605–614 (2006)

    Google Scholar 

  15. Gassner, E., Ionasec, R.I., Georgescu, B., Vogt, S., Schoepf, U., Comaniciu, D.: Performance of a dynamic aortic valve model for quantification of the opening area at cardiac mdct . comparison to manual planimetry. In: Radiological Society of North American (RSNA), Chicago, USA (2008)

    Google Scholar 

  16. Grbić, S., Ionasec, R., Vitanovski, D., Voigt, I., Wang, Y., Georgescu, B., Navab, N., Comaniciu, D.: Complete valvular heart apparatus model from 4D cardiac CT. Medical image computing and computer-assisted intervention: MICCAI. Int. Conf. Med. Image Comput. Computer-Assisted Intervention 13, 218–226 (2010)

    Google Scholar 

  17. Halpern, E.: Clinical Cardiac CT: Anatomy and Function. Thieme Medical Publishers, New York, USA (2008)

    Google Scholar 

  18. Hertz, T.: Learning distance functions: algorithms and applications. Ph.D. thesis. The Hebrew University of Jerusalem (2006)

    Google Scholar 

  19. Huang, J., Huang, X., Metaxas, D., Axel, L.: Dynamic texture based heart localization and segmentation in 4-d cardiac images. Biomedical Imaging From Nano to Macro 2007 ISBI 2007 4th IEEE International Symposium, pp. 852–855 (2007)

    Google Scholar 

  20. Ionasec, R.I., et al.: Robust motion estimation using trajectory spectrum learning: application to aortic and mitral valve modeling from 4d tee. In: Proceedings of the Int’l Conference Computer Vision (2009)

    Google Scholar 

  21. Ionasec, R.I., Georgescu, B., Comaniciu, D., Vogt, S., Schoepf, U., Gassner, E.: Patient specific 4d aortic root models derived from volumetric image data sets. Radiological Society of North American (RSNA), Chicago, USA (2008)

    Google Scholar 

  22. Ionasec, R.I., Georgescu, B., Gassner, E., Vogt, S., Kutter, O., Scheuering, M., Navab, N., Comaniciu, D.: Dynamic model-driven quantification and visual evaluation of the aortic valve from 4d ct. MICCAI 1, 686–694 (2008)

    Google Scholar 

  23. Ionasec, R.I., Voigt, I., Georgescu, B., Houle, H., Hornegger, J., Navab, N., Comaniciu, D.: Personalized modeling and assessment of the aortic-mitral coupling from 4D TEE and CT. In: MICCAI, Heidelberg, pp. 767–775 (2009)

    Google Scholar 

  24. Ionasec, R.I., Voigt, I., Georgescu, B., Wang, Y., Houle, H., Vega-Higuera, F., Navab, N., Comaniciu, D.: Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE. IEEE Trans. Med. Imaging 29, 1636–1651 (2010)

    Article  Google Scholar 

  25. Ionasec, R.I., Wang, Y., Georgescu, B., Voigt, I., Navab, N., Comaniciu, D.: Robust motion estimation using trajectory spectrum learning: application to aortic and mitral valve modeling from 4d tee. In: Proceedings of the 12th International Conference on Computer Vision (ICCV), IEEE, Kyoto, Japan (2009)

    Google Scholar 

  26. Jablokow, A.: National center for health statistics: national hospital discharge survey: annual summaries with detailed diagnosis and procedure data. Journal Data on Health Resources Utilization 13 (2009)

    Google Scholar 

  27. Kunzelman, K., Einstein, D., Cochran, R.: Fluid-structure interaction models of the mitral valve: function in normal and pathological states. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 362, 1393–1406 (2007)

    Article  Google Scholar 

  28. Lansac, E., Lim, H., Shomura, Y., Lim, K., Rice, N., Goetz, W., Acar, C., Duran, C.: A four-dimensional study of the aortic root dynamics. Eur. J. Cardio-Thorac. Surg: Official J. Eur. Assoc. Cardio-Thorac. Surg. 22, 497–503 (2002)

    Article  Google Scholar 

  29. Lloyd-Jones, D., Adams, R., Carnethon, M., De Simone, G., Ferguson, T.B., Flegal, K., Ford, E., Furie, K., Go, A., Greenlund, K., Haase, N., Hailpern, S., Ho, M., Howard, V., Kissela, B., Kittner, S., Lackland, D., Lisabeth, L., Marelli, A., McDermott, M., Meigs, J., Mozaffarian, D., Nichol, G., O’Donnell, C., Roger, V., Rosamond, W., Sacco, R., Sorlie, P., Stafford, R., Steinberger, J., Thom, T., Wasserthiel-Smoller, S., Wong, N., Wylie-Rosett, J., Hong, Y.: American heart association statistics committee and stroke statistics subcommittee, heart disease and stroke statistics–2009 update: a report from the american heart association statistics committee and stroke statistics subcommittee. Circulation 119, e21–e181 (2009)

    Article  Google Scholar 

  30. Lorenz, C., von Berg, J.: A comprehensive shape model of the heart. Med. Image Anal. 10, 657–670 (2006)

    Article  Google Scholar 

  31. Mutlak, D., Aronson, D., Lessick, J., Reisner, S., Dabbah, S., Agmon, Y.: Functional tricuspid regurgitation in patients with pulmonary hypertension. CHEST 135, 115–121 (2009)

    Article  Google Scholar 

  32. Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. ACM Trans. Graph. 21, 807–832 (2002)

    Article  Google Scholar 

  33. Park, J., Metaxas, D., Young, A., Axel, L.: Deformable models with parameter functions for cardiac motion analysis from tagged mri data. IEEE Trans. Med. Imaging 15, 278–289 (1996)

    Article  Google Scholar 

  34. Parr, J., Kirklin, J., Blackstone, E.: The early risk of re-replacement of aortic valves. Ann. Thorac. Surg. 23, 319–322 (1977)

    Article  Google Scholar 

  35. Peskin, C.S., McQueen, D.M.: Case Studies in Mathematical Modeling: Ecology, Physiology, and Cell Biology. Prentice-Hall, Englewood Cliffs, NJ, USA (1996)

    Google Scholar 

  36. Piazza, N., de Jaegere, P., Schultz, C., Becker, A., Serruys, P., Anderson, R.: Anatomy of the aortic valvar complex and its implications for transcatheter implantation of the aortic valve. Circ. Cardiovasc. Interventions 1, 74–81 (2008)

    Article  Google Scholar 

  37. Rueckert, D., Burger, P.: Geometrically deformable templates for shape-based segmentation and tracking in cardiac mr images. In: EMMCVPR ’97: Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer-Verlag, London, UK, pp. 83–98 (1997)

    Google Scholar 

  38. Schievano, S., Coats, L., Migliavacca, F., Norman, W., Frigiola, A., Deanfield, J., Bonhoeffer, P., Taylor, A.: Variations in right ventricular outflow tract morphology following repair of congenital heart disease: implications for percutaneous pulmonary valve implantation. J. Cardiovasc. Magn. Reson. 9, 687–695 (2007)

    Article  Google Scholar 

  39. Schievano, S., Migliavacca, F., Coats, S., Khambadkone, L., Carminati, M., Wilson, N., Deanfield, J., Bonhoeffer, P., Taylor, A.: Percutaneous pulmonary valve implantation based on rapid prototyping of right ventricular outflow tract and pulmonary trunk from mr data. Radiology 242, 490–499 (2007)

    Article  Google Scholar 

  40. Schneider, R.J., Perrin, D.P., Vasilyev, N.V., Marx, G.R., Del Nido, P.J., Howe, R.D.: Mitral annulus segmentation from 3d ultrasound using graph cuts. IEEE Trans. Med. Imaging 29, 1676–1687 (2010)

    Article  Google Scholar 

  41. Soncini, M., Votta, E., Zinicchino, S., Burrone, V., Mangini, A., Lemma, M., Antona, C., Redaelli, A.: Aortic root performance after valve sparing procedure: a comparative finite element analysis. Med. Eng. Phys. 31, 234–243 (2009)

    Article  Google Scholar 

  42. Staib, L.H., Duncan, J.S.: Model-based deformable surface finding for medical images. IEEE Trans. Med. Imaging 15, 720–731 (1996)

    Article  Google Scholar 

  43. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)

    Article  Google Scholar 

  44. Timek, T., Green, G., Tibayan, F., Lai, F., Rodriguez, F., Liang, D., Daughters, G., Ingels, N., Miller, D.: Aorto-mitral annular dynamics. Ann. Thorac. Surg. 76, 1944–1950 (2003)

    Article  Google Scholar 

  45. Tsymbal, A., Huber, M., Zhou, S.K.: Discriminative distance functions and the patient neighborhood graph for clinical decision support. Springer. Chapter Advances in Computational Biology, pp. 515–522 (2010)

    Google Scholar 

  46. Tu, Z.: Probabilistic boosting-tree: learning discriminative methods for classification, recognition, and clustering. ICCV 2, 1589–1596 (2005)

    Google Scholar 

  47. Veronesi, F., Corsi, C., Sugeng, L., Mor-Avi, V., Caiani, E., Weinert, L., Lamberti, C., Lang, R.: A study of functional anatomy of aortic-mitral valve coupling using 3D matrix transesophageal echocardiography. Circ. Cardiovasc. Imaging 2, 24–31 (2009)

    Article  Google Scholar 

  48. Veronesi, F., Corsi, C., Sugeng, L., Mor-Avi, V., Caiani, E., Weinert, L., Lamberti, C., Lang, R.M.: A study of functional anatomy of aortic-mitral valve coupling using 3D matrix transesophageal echocardiography. Circ. Cardiovasc. Imaging 2, 24–31 (2009)

    Article  Google Scholar 

  49. Vitanovski, D., Ionasec, R.I., Georgescu, B., Huber, M., Taylor, A., Hornegger, J., Comaniciu, D.: Personalized pulmonary trunk modeling for intervention planning and valve assessment estimated from ct data. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), London, USA, pp. 17–25 (2009)

    Google Scholar 

  50. Vitanovski, D., Tsymbal, A., Ionasec, R., Georgescu, B., Huber, M., Hornegger, J., Comaniciu, D.: Cross-modality assessment and planning for pulmonary trunk treatment using ct and mri imaging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Beijing, China (2010)

    Google Scholar 

  51. Votta, E., Caiani, E., Veronesi, F., Soncini, M., Montevecchi, F., Redaelli, A.: Mitral valve finite-element modelling from ultrasound data: a pilot study for a new approach to understand mitral function and clinical scenarios. Philos. Transact. A Math. Phys. Eng. Sci. 366, 3411–3434 (2008)

    Article  Google Scholar 

  52. Waechter, I., et al.: Patient specific models for planning and guidance of minimally invasive aortic valve implantation. In: MICCAI 2010. Springer Berlin/Heidelberg. Volume 6361 of Lecture Notes in Computer Science, pp. 526–533 (2010)

    Google Scholar 

  53. Watanabe, N., Ogasawara, Y., Yamaura, Y., Kawamoto, T., Toyota, E., Akasaka, T., Yoshida, K.: Quantitation of mitral valve tenting in ischemic mitral regurgitation by transthoracic real-time three-dimensional echocardiography. J. Am. Coll. Cardiol. 45, 763–769 (2005)

    Article  Google Scholar 

  54. Webb, G.I.: Multiboosting: a technique for combining boosting and wagging. Mach. Learn. 40, 159–196 (2000)

    Article  Google Scholar 

  55. Yang, L., Georgescu, B., Zheng, Y., Meer, P., Comaniciu, D.: 3d ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  56. Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3d cardiac ct volumes using marginal space learning and steerable features. IEEE TMI 27, 1668–1681 (2008)

    Google Scholar 

  57. Zhou, S.K., Georgescu, B., Zhou, X.S., Comaniciu, D.: Image based regression using boosting method. ICCV 1, 541–548 (2005)

    Google Scholar 

  58. Zhuang, X., Rhode, K.S., Razavi, R.S., Hawkes, D.J., Ourselin, S.: A registration-based propagation framework for automatic whole heart segmentation of cardiac mri. IEEE Trans. Med. Imaging 29, 1612–1625 (2010)

    Article  Google Scholar 

  59. Zhuang, X., Yao, C., Ma, Y.L., Hawkes, D., Penney, G., Ourselin, S.: Registration-based propagation for whole heart segmentation from compounded 3D echocardiography. IEEE pp. 1093–1096 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA

    Razvan Ioan Ionasec, Dime Vitanovski & Dorin Comaniciu

Authors
  1. Razvan Ioan Ionasec
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dime Vitanovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dorin Comaniciu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Razvan Ioan Ionasec .

Editor information

Editors and Affiliations

  1. Fac. Engineering, Dept. Biomedical Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Amit Gefen

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ioan Ionasec, R., Vitanovski, D., Comaniciu, D. (2011). Morphologica l and Functional Modeling of the Heart Valves and Chambers. In: Gefen, A. (eds) Patient-Specific Modeling in Tomorrow's Medicine. Studies in Mechanobiology, Tissue Engineering and Biomaterials, vol 09. Springer, Berlin, Heidelberg. https://doi.org/10.1007/8415_2011_94

Download citation

  • DOI: https://doi.org/10.1007/8415_2011_94

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24617-3

  • Online ISBN: 978-3-642-24618-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation