Advertisement

Advanced Transcatheter Aortic Valve Implantation (TAVI) Planning from CT with ShapeForest

  • Joshua K. Y. Swee
  • Saša Grbić
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Transcatheter aortic valve implantation (TAVI) is becoming a standard treatment for non-operable and high-risk patients with symptomatic severe aortic valve stenosis. As there is no direct view or access to the affected anatomy, comprehensive preoperative planning is crucial for a successful outcome, with the most important decisions made during planning being the selection of the proper implant size, and determining the correct C-arm angulations. While geometric models extracted from 3D images are often used to derive these measurements, the complex shape variation of the AV anatomy found in these patients causes many of the shape representations used to estimate such geometric models to fail in capturing morphological characteristics in sufficient detail. In addition, most current approaches only model the aortic valve (AV), omitting modeling the left ventricle outflow tract (LVOT) entirely despite its high correlation with severe complications such as annulus ruptures, paravalvular leaks and myocardial infarction. We propose a fully automated method to extract patient specific models of the AV and the LVOT, and derive comprehensive biomarkers for accurate TAVI planning. We utilize a novel shape representation – the ShapeForest – which is able to model complex shape variation, preserves local shape information, and incorporates prior knowledge during shape space inference. Extensive quantitative and qualitative experiments performed on 630 volumetric data sets demonstrate an accuracy of 0.69 mm for the AV and 0.83 mm for the LVOT, an improvement of over 16% and 18% respectively when compared against state of the art methods.

Keywords

Aortic Valve Transcatheter Aortic Valve Implantation Transcatheter Aortic Valve Replacement Aortic Valve Disease Shape Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    John, D., Buellesfeld, L., Yuecel, S., Mueller, R., Latsios, G., Beucher, H., Gerckens, U., Grube, E.: Correlation of Device landing zone calcification and acute procedural success in patients undergoing transcatheter aortic valve implantations with the self-expanding CoreValve prosthesis. JACC: Cardiovascular interventions 3(2), 233–243 (2010)CrossRefGoogle Scholar
  2. 2.
    Kodali, S.K., Williams, M.R., Smith, C.R., Svensson, L.G., Webb, J.G., Makkar, R.R., Fontana, G.P., Dewey, T.M., Thourani, V.H., Pichard, A.D., Fischbein, M., Szeto, W.Y., Lim, S., Greason, P.S., Malaisrie, S.C., Douglas, P.S., Hahn, R.T., Whisenant, D., Akin, J.J., Anderson, W.N., Leon, M.B.: Two-year outcomes after transcatheter or surgical aortic-valve replacement. NEJM 366(18), 1686–1695 (2012)CrossRefGoogle Scholar
  3. 3.
    Reardon, M.J.: Transcatheter aortic valve replacement: Indications and beyond the clinical trials. Texas Heart Institute Journal 40(5), 583–586 (2013)Google Scholar
  4. 4.
    Conti, C.A., Stevanella, M., Maffessanti, F., Trunfio, S., Votta, E., Roghi, A., Parodi, O., Caiani, E.G., Redaell, A.: Mitral valve modelling in ischemic patients: Finite element analysis from cardiac magnetic resonance imaging. In: Computing in Cardiology, pp. 1059–1062 (2010)Google Scholar
  5. 5.
    Ionasec, R.I., Voigt, I., Georgescu, B., Wang, Y., Houle, H., Higuera, F., Navab, N., Comaniciu, D.: Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE. TMI 29(9), 1636–1651 (2010)Google Scholar
  6. 6.
    Waechter, I., Kneser, R., Korosoglou, G., Peters, J., Bakker, N.H., Van Der Boomen, R., Weese, J.: Patient specific models for planning and guidance of minimally invasive aortic valve implantation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 526–533. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Grbic, 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 Analysis 16(5), 1003–1014 (2012)CrossRefGoogle Scholar
  8. 8.
    Zheng, Y., Georgescu, B., Ling, H., Zhou, S.K., Scheuering, M., Comaniciu, D.: Constrained marginal space learning for efficient 3d anatomical structure detection in medical images. In: CVPR, pp. 194–201 (2009)Google Scholar
  9. 9.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefGoogle Scholar
  10. 10.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  11. 11.
    Powell, M.J.: A fast algorithm for nonlinearly constrained optimization calculations. In: Numerical Analysis, pp. 144–157 (1978)Google Scholar
  12. 12.
    Cootes, T.F., Ionita, M.C., Lindner, C., Sauer, P.: Robust and accurate shape model fitting using random forest regression voting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 278–291. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Joshua K. Y. Swee
    • 1
  • Saša Grbić
    • 1
    • 2
  1. 1.Imaging and Computer VisionSiemens Corporation, Corporate TechnologyPrincetonUSA
  2. 2.Computer Aided Medical ProceduresTechnical University MunichGermany

Personalised recommendations