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Results for “Aortic Valve/ultrasonography”

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Quantification of mitral regurgitation by automated cardiac output measurement: experimental and clinical validation

OBJECTIVES: To develop and validate an automated noninvasive method to quantify mitral regurgitation. BACKGROUND: Automated cardiac output measurement (ACM), which integrates digital color Doppler velocities in space and in time, has been validated for the left ventricular (LV) outflow tract but has not been tested for the LV inflow tract or to assess mitral regurgitation (MR). METHODS: First, to validate ACM against a gold standard (ultrasonic flow meter), 8 dogs were studied at 40 different stages of cardiac output (CO). Second, to compare ACM to the LV outflow (ACMa) and inflow (ACMm) tracts, 50 normal volunteers without MR or aortic regurgitation (44+/-5 years, 31 male) were studied. Third, to compare ACM with the standard pulsed Doppler-two-dimensional echocardiographic (PD-2D) method for quantification of MR, 51 patients (61+/-14 years, 30 male) with MR were studied. RESULTS: In the canine studies, CO by ACM (1.32+/-0.3 liter/min, y) and flow meter (1.35+/-0.3 liter/min, x) showed good correlation (r=0.95, y=0.89x+0.11) and agreement (deltaCO(y-x)=0.03+/-0.08 [mean+/-SD] liter/min). In the normal subjects, CO measured by ACMm agreed with CO by ACMa (r=0.90, p < 0.0001, deltaCO=-0.09+/-0.42 liter/min), PD (r=0.87, p < 0.0001, deltaCO=0.12+/-0.49 liter/min) and 2D (r=0.84, p < 0.0001, deltaCO=-0.16+/-0.48 liter/min). In the patients, mitral regurgitant volume (MRV) by ACMm-ACMa agreed with PD-2D (r= 0.88, y=0.88x+6.6, p < 0.0001, deltaMRV=2.68+/-9.7 ml). CONCLUSIONS: We determined that ACM is a feasible new method for quantifying LV outflow and inflow volume to measure MRV and that ACM automatically performs calculations that are equivalent to more time-consuming Doppler and 2D measurements. Additionally, ACM should improve MR quantification in routine clinical practice.

Non-NASA Center↗

Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates

This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability.

Non-NASA Center↗