Background Valve repair for ischemic mitral regurgitation (IMR) with undersized annuloplasty rings is characterized by high IMR recurrence rates. Patient-specific preoperative imaging-based risk stratification for recurrent IMR would optimize results. We sought to determine if pre-repair three-dimensional (3D) echocardiography combined with a novel valve modeling algorithm would be predictive of IMR recurrence 6 months after repair. Methods Intraoperative transesophageal real-time 3D echocardiography was performed in 50 patients undergoing undersized ring annuloplasty for IMR (and in 21 patients with normal mitral valves). A customized image analysis protocol was used to assess 3D annular geometry and regional leaflet tethering. IMR recurrence (≥grade 2) was assessed with two-dimensional transthoracic echocardiography 6 months after repair. Results Preoperative annular geometry was similar in all IMR patients; and preoperative leaflet tethering was significantly higher in patients with recurrent IMR (n=13) as compared with patients in whom IMR did not recur IMR (n=37) (tethering index 3.91±1.01 vs. 2.90±1.17, P=0.008; tethering angles of A3 (23.5±8.9° vs. 14.4± 11.4°, P=0.012), P2 (44.4±8.8° vs. 28.2±17.0°, P=0.002), and P3 (35.2±6.0° vs. 18.6±12.7°, P<0.001)). Multivariate logistic regression analysis revealed preoperative P3 tethering angle as an independent predictor of IMR recurrence with an optimal cut-off value of 29.9° (AUC 0.92, 95%CI 0.84–1.00, P<0.001). Conclusions 3D echocardiography combined with valve modeling is predictive of recurrent IMR. Preoperative regional leaflet tethering of segment P3 is a strong independent predictor of IMR recurrence after undersized ring annuloplasty. In patients with a preoperative P3 tethering angle ≥29.9° chordal-sparing valve replacement rather than valve repair should be strongly considered.

Background-A comprehensive three-dimensional echocardiography based approach is applied to preoperative mitral valve (MV) analysis in patients with ischemic mitral regurgitation (IMR). This method is used to characterize the heterogeneous nature of the pathologic anatomy associated with IMR.

Comprehensive visual and quantitative analysis of in vivo human mitral valve morphology is central to the diagnosis and surgical treatment of mitral valve disease. Real-time 3D transesophageal echocardiography (3D TEE) is a practical, highly informative imaging modality for examining the mitral valve in a clinical setting. To facilitate visual and quantitative 3D TEE image analysis, we describe a fully automated method for segmenting the mitral leaflets in 3D TEE image data. The algorithm integrates complementary probabilistic segmentation and shape modeling techniques (multi-atlas joint label fusion and deformable modeling with continuous medial representation) to automatically generate 3D geometric models of the mitral leaflets from 3D TEE image data. These models are unique in that they establish a shape-based coordinate system on the valves of different subjects and represent the leaflets volumetrically, as structures with locally varying thickness. In this work, expert image analysis is the gold standard for evaluating automatic segmentation. Without any user interaction, we demonstrate that the automatic segmentation method accurately captures patient-specific leaflet geometry at both systole and diastole in 3D TEE data acquired from a mixed population of subjects with normal valve morphology and mitral valve disease.

Background-Real-time three-dimensional (3D) echocardiography has the ability to construct quantitative models of the mitral valve (MV). Imaging and modeling algorithms rely on operator interpretation of raw images and may be subject to observer-dependent variability. We describe a comprehensive analysis technique to generate high-resolution 3D MV models and examine interoperator and intraoperator repeatability in humans.

Purpose Advances in mitral valve repair and adoption have been partly attributed to improvements in echocardiographic imaging technology. To further educate and guide repair surgery, we have developed a methodology to quickly produce physical models of the valve using novel 3D echocardiographic imaging software in combination with stereolithographic printing. Description Quantitative virtual mitral valve shape models were developed from 3D transesophageal echocardiographic images using software based on semi-automated image segmentation and continuous medial representation (cm-rep) algorithms. These quantitative virtual shape models were then used as input to a commercially available stereolithographic printer to generate a physical model of the each valve at end systole and end diastole. Evaluation Physical models of normal and diseased valves (ischemic mitral regurgitation and myxomatous degeneration) were constructed. There was good correspondence between the virtual shape models and physical models. Conclusions It was feasible to create a physical model of mitral valve geometry under normal, ischemic and myxomatous valve conditions using 3D printing of 3D echocardiographic data. Printed valves have the potential to guide surgical therapy for mitral valve disease.

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Purpose: Precise 3D modeling of the mitral valve has the potential to improve our understanding of valve morphology, particularly in the setting of mitral regurgitation (MR). Toward this goal, the authors have developed a user-initialized algorithm for reconstructing valve geometry from transesophageal 3D ultrasound (3D US) image data. Methods: Semi-automated image analysis was performed on transesophageal 3D US images obtained from 14 subjects with MR ranging from trace to severe. Image analysis of the mitral valve at midsystole had two stages: user-initialized segmentation and 3D deformable modeling with continuous medial representation (cm-rep). Semi-automated segmentation began with useridentification of valve location in 2D projection images generated from 3D US data. The mitral leaflets were then automatically segmented in 3D using the level set method. Second, a bileaflet deformable medial model was fitted to the binary valve segmentation by Bayesian optimization. The resulting cm-rep provided a visual reconstruction of the mitral valve, from which localized measurements of valve morphology were automatically derived. The features extracted from the fitted cm-rep included annular area, annular circumference, annular height, intercommissural width, septolateral length, total tenting volume, and percent anterior tenting volume. These measurements were compared to those obtained by expert manual tracing. Regurgitant orifice area (ROA) measurements were compared to qualitative assessments of MR severity. The accuracy of valve shape representation with cm-rep was evaluated in terms of the Dice overlap between the fitted cm-rep and its target segmentation. Results: The morphological features and anatomic ROA derived from semi-automated image analysis were consistent with manual tracing of 3D US image data and with qualitative assessments of MR severity made on clinical radiology. The fitted cm-reps accurately captured valve shape and demonstrated patient-specific differences in valve morphology among subjects with varying degrees of MR severity. Minimal variation in the Dice overlap and morphological measurements was observed when different cm-rep templates were used to initialize model fitting. Conclusions: This study demonstrates the use of deformable medial modeling for semi-automated 3D reconstruction of mitral valve geometry using transesophageal 3D US. The proposed algorithm provides a parametric geometrical representation of the mitral leaflets, which can be used to evaluate valve morphology in clinical ultrasound images.

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