An integrated framework for finite-element modeling of mitral valve biomechanics from medical images: Application to MitralClip intervention planning
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- 作者：Tommaso Mansi ; Ingmar Voigt ; Bogdan Georgescu ; Xudong Zheng ; Etienne Assoumou Mengue ; Michael Hackl ; Razvan I. Ionasec ; Thilo Noack ; Joerg Seeburger ; Dorin Comaniciu
- 关键词：Mitral valve apparatus ; Patient-specific anatomy ; Machine-learning ; Finite-element biomechanical models ; Mitral valve therapy planning
- 刊名：Medical Image Analysis
- 出版年：2012
- 出版时间：October, 2012
- 年：2012
- 卷：16
- 期：7
- 页码：1330-1346
- 全文大小：4156 K
文摘
Treatment of mitral valve (MV) diseases requires comprehensive clinical evaluation and therapy personalization to optimize outcomes. Finite-element models (FEMs) of MV physiology have been proposed to study the biomechanical impact of MV repair, but their translation into the clinics remains challenging. As a step towards this goal, we present an integrated framework for finite-element modeling of the MV closure based on patient-specific anatomies and boundary conditions. Starting from temporal medical images, we estimate a comprehensive model of the MV apparatus dynamics, including papillary tips, using a machine-learning approach. A detailed model of the open MV at end-diastole is then computed, which is finally closed according to a FEM of MV biomechanics. The motion of the mitral annulus and papillary tips are constrained from the image data for increased accuracy. A sensitivity analysis of our system shows that chordae rest length and boundary conditions have a significant influence upon the simulation results. We quantitatively test the generalization of our framework on 25 consecutive patients. Comparisons between the simulated closed valve and ground truth show encouraging results (average point-to-mesh distance: 1.49 ¡À 0.62 mm) but also the need for personalization of tissue properties, as illustrated in three patients. Finally, the predictive power of our model is tested on one patient who underwent MitralClip by comparing the simulated intervention with the real outcome in terms of MV closure, yielding promising prediction. By providing an integrated way to perform MV simulation, our framework may constitute a surrogate tool for model validation and therapy planning.