Michael Cardinale

Characterization of degenerative mitral valve disease using morphologic analysis of real-time three-dimensional echocardiographic images: objective insight into complexity and planning of mitral valve repair.

Background—Presurgical planning of mitral valve (MV) repair in patients with Barlow disease (BD) and fibroelastic deficiency (FED) is challenging because of the inability to assess accurately the complexity of MV prolapse. We hypothesized that the etiology of degenerative MV disease (DMVD) could be objectively and accurately ascertained using parameters of MV geometry obtained by morphological analysis of real-time 3D echocardiographic (RT3DE) images. Methods and Results—Seventy-seven patients underwent transesophageal RT3DE study: 57 patients with DMVD studied intraoperatively (28 BD, 29 FED classified during surgery) and 20 patients with normal MV who were used as control subjects (NL). MVQ software (Philips) was used to measure parameters of annular dimensions and geometry and leaflet surface area, including billowing volume and height. The Student t test and multinomial logistic regression was performed to identify parameters best differentiating DMVD patients from normal as well as FED from BD. Morphological analysis in the DMVD group revealed a progressive increase in multiple parameters from NL to FED to BD, allowing for accurate diagnosis of these entities. The strongest predictors of the presence of DMVD included billowing height and volume. Three-dimensional billowing height with a cutoff value of 1.0 mm differentiated DMVD from NL without overlap, and billowing volume with a cutoff value 1.15 mL differentiated between FED and BD without overlap. Conclusions—Morphological analysis as a form of decision support in assessing MV billowing revealed significant quantifiable differences between NL, FED, and BD patients, allowing accurate classification of the etiology of MV prolapse and determination of the anticipated complexity of repair.Pre-surgical planning of mitral valve (MV) repair in patients with Barlows disease (BD) and fibroelastic deficiency (FED) is challenging due to inability to accurately assess the complexity of MV prolapse. We hypothesized that the etiology of degenerative MV disease (DMVD) could be objectively and accurately determined using morphologic analysis of MV geometry from real-time 3D echocardiographic (RT3DE) images. Seventy-seven patients underwent transesophageal RT3DE study: 57 patients with DMVD studied intra-operatively (28 BD, 29 FED classified during surgery) and 20 patients with normal MV who were used as controls (NL). Parameters of annular dimensions and geometry, and leaflet surface area were measured. Morphologic analysis in the DMVD group revealed a progressive increase in multiple parameters from NL to FED to BD, allowing for accurate diagnosis of these entities. Strongest predictors of the presence of DMVD included billowing height and volume. 3D billowing height with a cutoff value of 1.0 mm differentiated DMVD from NL without overlap, and billowing volume with a cutoff value 1.15 ml differentiated between FED and BD without overlap. Morphologic analysis as a form of decision support of assessing MV billowing revealed significant quantifiable differences between NL, FED and Barlow, allowing accurate classification of the etiology of MV prolapse and determination of the anticipated complexity of repair‥

Characterization of Degenerative Mitral Valve Disease Using Morphologic Analysis of Real-Time 3D Echocardiographic Images: Objective Insight into Complexity and Planning of Mitral Valve Repair

Background—Presurgical planning of mitral valve (MV) repair in patients with Barlow disease (BD) and fibroelastic deficiency (FED) is challenging because of the inability to assess accurately the complexity of MV prolapse. We hypothesized that the etiology of degenerative MV disease (DMVD) could be objectively and accurately ascertained using parameters of MV geometry obtained by morphological analysis of real-time 3D echocardiographic (RT3DE) images. Methods and Results—Seventy-seven patients underwent transesophageal RT3DE study: 57 patients with DMVD studied intraoperatively (28 BD, 29 FED classified during surgery) and 20 patients with normal MV who were used as control subjects (NL). MVQ software (Philips) was used to measure parameters of annular dimensions and geometry and leaflet surface area, including billowing volume and height. The Student t test and multinomial logistic regression was performed to identify parameters best differentiating DMVD patients from normal as well as FED from BD. Morphological analysis in the DMVD group revealed a progressive increase in multiple parameters from NL to FED to BD, allowing for accurate diagnosis of these entities. The strongest predictors of the presence of DMVD included billowing height and volume. Three-dimensional billowing height with a cutoff value of 1.0 mm differentiated DMVD from NL without overlap, and billowing volume with a cutoff value 1.15 mL differentiated between FED and BD without overlap. Conclusions—Morphological analysis as a form of decision support in assessing MV billowing revealed significant quantifiable differences between NL, FED, and BD patients, allowing accurate classification of the etiology of MV prolapse and determination of the anticipated complexity of repair.Pre-surgical planning of mitral valve (MV) repair in patients with Barlows disease (BD) and fibroelastic deficiency (FED) is challenging due to inability to accurately assess the complexity of MV prolapse. We hypothesized that the etiology of degenerative MV disease (DMVD) could be objectively and accurately determined using morphologic analysis of MV geometry from real-time 3D echocardiographic (RT3DE) images. Seventy-seven patients underwent transesophageal RT3DE study: 57 patients with DMVD studied intra-operatively (28 BD, 29 FED classified during surgery) and 20 patients with normal MV who were used as controls (NL). Parameters of annular dimensions and geometry, and leaflet surface area were measured. Morphologic analysis in the DMVD group revealed a progressive increase in multiple parameters from NL to FED to BD, allowing for accurate diagnosis of these entities. Strongest predictors of the presence of DMVD included billowing height and volume. 3D billowing height with a cutoff value of 1.0 mm differentiated DMVD from NL without overlap, and billowing volume with a cutoff value 1.15 ml differentiated between FED and BD without overlap. Morphologic analysis as a form of decision support of assessing MV billowing revealed significant quantifiable differences between NL, FED and Barlow, allowing accurate classification of the etiology of MV prolapse and determination of the anticipated complexity of repair‥

Geometric Assessment of Regional Left Ventricular Remodeling by Three-Dimensional Echocardiographic Shape Analysis Correlates with Left Ventricular Function

BACKGROUND Left ventricular (LV) volumes and ejection fraction derived from two-dimensional echocardiography are two measures of adverse LV remodeling, which predict survival in patients with systolic heart failure. However, the geometric assumptions and image foreshortening that can occur with two-dimensional echocardiography reduces measurement accuracy and thus predictive value. By its nature, three-dimensional (3D) echocardiography allows the entire LV shape to be studied, providing a methodology to examine LV remodeling through LV curvature on a global and regional scale. The aim of this study was to correlate changes in global and regional LV shape to LV ejection fraction. METHODS Full-volume, 3D transthoracic echocardiographic studies of the left ventricle were performed in 106 consecutive patients with either normal left ventricles (n = 59) or cardiomyopathies (n = 47). Customized software (QLAB) was used to extract segmented 3D LV endocardial shells at end-systole and end-diastole and to analyze these shells to determine global and regional LV shape analysis. Independent t tests were used for intergroup comparisons, and linear regression was used to correlate regional shape changes with systolic performance. RESULTS Derivation and analysis of the 3D LV shells was possible in all patients. Patients with dilated cardiomyopathy had significantly smaller curvature values, indicating rounder global LV shape throughout the cardiac cycle. Regional analysis identified a loss of septal and apical curvatures in these patients. Systolic apical mean curvature was well correlated with LV ejection fraction (r = 0.89). CONCLUSIONS This is the first study to demonstrate that regional remodeling measured by regional 3D LV curvature correlates well with LV function. As well, this methodology is independent of the geometric assumptions that limit the predictive value of two-dimensional echocardiographic measures of LV remodeling. Overall, this is a novel tool that may have applications in the assessment and prediction of outcomes of different forms of dilated cardiomyopathy.

FULLY AUTOMATED QUANTIFICATION OF LEFT VENTRICULAR AND LEFT ATRIAL VOLUMES FROM TRANSTHORACIC 3D ECHOCARDIOGRAPHY: A VALIDATION STUDY

Cardiac chamber quantification from 3D transthoracic echocardiography (3D TTE) has been shown to be superior to measurements obtained from 2D studies. However, integration of 3D TTE into routine clinical practice has been limited by the time-consuming workflow and need for 3D expertise. We assessed

Novel method of measuring valvular regurgitation using three-dimensional nonlinear curve fitting of doppler signals within the flow convergence zone

Mitral valve regurgitation (MR) is among the most prevalent and significant valve problems in the Western world. Echocardiography plays a significant role in the diagnosis of degenerative valve disease. However, a simple and accurate means of quantifying MR has eluded both the technical and clinical ultrasound communities. Perhaps the best clinically accepted method used today is the 2-D proximal isovelocity surface area (PISA) method. In this study, a new quantification method using 3-D color Doppler ultrasound, called the field optimization method (FOM), is described. For each 3-D color flow volume, this method iterates on a simple fluid dynamics model that, when processed by a model of ultrasound physics, attempts to agree with the observed velocities in a least-squares sense. The output of this model is an estimate of the regurgitant flow and the location of its associated orifice. To validate the new method, in vitro experiments were performed using a pulsatile flow loop and different geometric orifices. Measurements from the FOM and from 2-D PISA were compared with measurements made with a calibrated ultrasonic flow probe. Results show that the new method has a higher correlation to the truth data and has lower inter- and intra-observer variability than the 2-D PISA method.

Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms

Abstract. Coarctation of aorta (CoA) is a critical congenital heart defect (CCHD) that requires accurate and immediate diagnosis and treatment. Current newborn screening methods to detect CoA lack both in sensitivity and specificity, and when suspected in a newborn, it must be confirmed using specialized imaging and expert diagnosis, both of which are usually unavailable at tertiary birthing centers. We explore the feasibility of applying machine learning methods to reliably determine the presence of this difficult-to-diagnose cardiac abnormality from ultrasound image data. We propose a framework that uses deep learning-based machine learning methods for fully automated detection of CoA from two-dimensional ultrasound clinical data acquired in the parasternal long axis view, the apical four chamber view, and the suprasternal notch view. On a validation set consisting of 26 CoA and 64 normal patients our algorithm achieved a total error rate of 12.9% (11.5% false-negative error and 13.6% false-positive error) when combining decisions of classifiers over three standard echocardiographic view planes. This compares favorably with published results that combine clinical assessments with pulse oximetry to detect CoA (71% sensitivity).