Semi-automated mitral valve morphometry and computational stress analysis using 3D ultrasound
Introduction
Mitral valve (MV) disease is common in humans and not infrequently fatal. Mitral regurgitation, in particular, demonstrates a strongly-graded relationship between severity and reduced survival (Trichon et al., 2003). MV surgery, both repair and replacement, are commonly exercised treatment options for mitral regurgitation. Imaging and assessment of the mitral valve has traditionally been achieved by qualitative 2D ultrasound image analysis. Recently, real-time three-dimensional transesophageal echocardiography (rt-3DTEE) has become widely available and implemented. 3D image-based modeling of the mitral valve is increasingly useful, and finite element analysis (FEA) has been applied to the MV frequently over the last 20 years (Kunzelman et al., 1993, Kunzelman et al., 2007, Prot et al., 2009, Votta et al., 2008).
To date, the majority of valve morphometry studies have employed manual tracing to reconstruct valve geometry from 3D echocardiographic image data (Jassar et al., 2011, Levine et al., 1989, Ryan et al., 2007, Sugeng et al., 2009, Vergnat et al., 2011, Yamaura et al., 2004). Therefore, the first objective of this paper is to introduce an alternative semi-automated approach to valve morphometry based on a simple and rapid approach to user-initialized image segmentation that exploits the contrast between the mitral valve tissue and surrounding blood pool in rt-3DTEE images. The valve is subsequently modeled using 3D continuous medial representation to obtain localized thickness maps of the mitral leaflets.
Our laboratory has recently provided a framework for the application of in vivo MV geometry and FEA to human MV physiology, pathophysiology, and repair (Xu et al., 2010). Therefore, the second objective of the current study is to demonstrate that the semi-automated 3D MV model can be loaded with physiologic pressures using FEA, yielding reasonable and meaningful stress and strain magnitudes and distributions. Furthermore, we endeavor to demonstrate this capability in both healthy and diseased human mitral valves.
Section snippets
Image acquisition
Intra-operative rt-3DTEE data sets were obtained from two patients, one with severe ischemic mitral regurgitation (IMR) and one without mitral valve disease. The electrocardiographically gated images were acquired with an iE33 scanner (Philips Medical Systems, Andover, MA) using a 2 to 7 MHz transesophageal matrix-array transducer over four consecutive cardiac cycles. The frame rate was 17 to 30 Hz with an imaging depth of 14 to 17 cm. The image volumes were exported in Cartesian format
Results
The mean and maximal AL and PL thicknesses derived from 3DE are reported in Table 2, for both the normal and the diseased MV. The regurgitant orifice of the diseased valve was clearly imaged and depicted in the 3D model, as demonstrated in Fig. 4.
The pressure-load-induced von Mises stress fields predicted by FEA are presented in Fig. 5, for both the normal and diseased MV. The regurgitant orifice of the diseased valve is evident in the loaded valve, though some conformational change
Discussion
A semi-automated and integrated methodology for imaging, segmenting, modeling, and deriving computationally-predicted pressure-derived MV leaflet stresses is presented herein, and points the way towards intraoperative and periprocedural guidance from morphometric and stress modeling of the MV.
The current study describes an approach to valve morphometry that provides a comprehensive, automated assessment of 3D valve geometry – in both normal and diseased MVs – by ultrasound image analysis. This
Conflict of Interest
The authors have no commercial relationships which could lead to a conflict of interest relative to this work.
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