Robust Physically-Constrained Modeling of the Mitral Valve and Subvalvular Apparatus

  • Ingmar Voigt
  • Tommaso Mansi
  • Razvan Ioan Ionasec
  • Etienne Assoumou Mengue
  • Helene Houle
  • Bogdan Georgescu
  • Joachim Hornegger
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Mitral valve (MV) is often involved in cardiac diseases, with various pathological patterns that require a systemic view of the entire MV apparatus. Due to its complex shape and dynamics, patient-specific modeling of the MV constitutes a particular challenge. We propose a novel approach for personalized modeling of the dynamic MV and its subvalvular apparatus that ensures temporal consistency over the cardiac sequence and provides realistic deformations. The idea is to detect the anatomical MV components under constraints derived from the biomechanical properties of the leaflets. This is achieved by a robust two-step alternate algorithm that combines discriminative learning and leaflet biomechanics. Extensive evaluation on 200 transesophageal echochardiographic sequences showed an average Hausdorff error of 5.1mm at a speed of 9sec, which constitutes an improvement of up to 11.5% compared to purely data driven approaches. Clinical evaluation on 42 subjects showed, that the proposed fully-automatic approach could provide discriminant biomarkers to detect and quantify remodeling of annulus and leaflets in functional mitral regurgitation.

Keywords

Mitral Valve Mitral Annulus Biomechanical Model Functional Mitral Regurgitation Statistical Shape Model 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ingmar Voigt
    • 1
    • 2
  • Tommaso Mansi
    • 1
  • Razvan Ioan Ionasec
    • 1
  • Etienne Assoumou Mengue
    • 1
  • Helene Houle
    • 3
  • Bogdan Georgescu
    • 1
  • Joachim Hornegger
    • 2
  • Dorin Comaniciu
    • 1
  1. 1.Image Analytics and InformaticsSiemens Corporate ResearchPrincetonUSA
  2. 2.Pattern Recognition LabUniversity of Erlangen-NurembergGermany
  3. 3.Ultrasound, PLM-Innovation, Siemens HealthcareUSA

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