Hisakazu Uehara

Can the proximal isovelocity surface area method calculate stenotic mitral valve area in patients with associated moderate to severe aortic regurgitation? Analysis using low aliasing velocity of 10% of the peak transmitral velocity.

To assess the ability of the proximal isovelocity surface area (PISA) method to accurately measure the stenotic mitral valve area (MVA), and to assess whether aortic regurgitation (AR) affects the calculation, we compared the accuracy of the PISA method and the pressure half‐time (PHT) method for determining MVA in patients with and without associated AR by using two‐dimensional echocardiographic planimetry as a standard. The study population consisted of 45 patients with mitral stenosis. Seventeen of the 45 patients had associated moderate‐to‐severe AR. The PISA method was performed using low aliasing velocity (AV) of 10% of the peak transmitral velocity, which provided the most accurate estimation of MVA when compared with planimetry. The maximal radius r of the PISA was measured from the orifice to blue‐red aliasing interface. Using the PISA method, MVA was calculated as (2πr2) ×θ/ 180 × AV/Vmax, where θ was the inflow angle formed by mitral leaflets, AV was the aliasing velocity (cm/sec), and Vmax was the peak transmitral velocity (cm/sec). MVA by the PISA method correlated well with planimetry both in patients with AR (r = 0.90, P < 0.001, SEE = 0.17 cm2) and without AR (r = 0.92, P < 0.001, SEE = 0.16 cm2). However, MVA by the PHT method did not correlate as well with planimetry (r = 0.57, P < 0.05, SEE = 0.37 cm2) in patients with associated AR, and the PHT method produced a significant overestimation (24%) of MVA obtained by planimetry in these patients. We conclude that the PISA method allows accurate estimation of MVA and is not influenced by AR.

Differentiation of myocardial infarction and angina pectoris by processing ultrasonic color kinesis images.

PurposeThe aim of this study was to develop a method for early, accurate differentiation between old myocardial infarction (OMI) and angina pectoris (AP) using color kinesis (CK) images. We first extracted exact end-diastolic and end-systolic contours from CK images and then extracted the features of cardiac function from two CK images (one at rest, the other after exercise) and investigated their effectiveness in differentiating old myocardial infarction and angina pectoris. We then evaluated the effectiveness of several features in recognizing coronary artery disease and used the effective features to show the differentiation results.MethodsFirst, we extracted exact end-diastolic and end-systolic contours from CK images with an active contour model. Second, we defined the features that seemed to be effective in recognizing coronary artery disease. The features are extracted from the region between the end-diastolic endocardial contour and end-systolic endocardial contour in two CK images: one obtained when the subject was at rest and the other after exercise. Nine features were considered effective for differentiating old myocardial infarction and angina pectoris, and the effectiveness in recognizing coronary artery disease, which includes old myocardial infarction and angina pectoris, was evaluated. Third, coronary artery disease is recognized by the effective features.ResultsContours near a manual trace by a skilled physician were obtained using the proposed method. Multiple comparisons of the mean values of the extracted features were drawn among three groups: a healthy-subject group; an old myocardial infarction patient group; and an angina pectoris patient group. The feature effective in differentiating old myocardial infarction was the “area at rest”; those effective in differentiating angina pectoris were a “decrease in area” and a “decrease in movement.” These effective features have almost always differentiated old myocardial infarction and angina pectoris.ConclusionsThis study used the endocardial contour extraction technique with the dynamic contour model and evaluated the validity of the features of cardiac function; it then recognized coronary artery disease from the effective features. Multiple comparisons of the mean value of the extracted features among the healthy-subject group, the old myocardial infarction patient group, and the angina pectoris patient group has proved that the “area at rest” is effective in differentiating old myocardial infarction, and the “decrease in area” and “decrease in movement” are effective for differentiating angina pectoris.

Diagnosis of ischemic heart disease with exercise echocardiography: Comparison of images obtained at peak- and post-exercise

Peak-and post-exercise stress echocardiography were compared with respect to ability to detect coronary artery disease in 138 consecutive patients undergoing supine bicycle stress echocardiography. Sixty of these patients had single-vessel disease; 37, double-vessel disease; and 19, triple-vessel disease. Exercise was performed in the 20- to 30-degree left decubitus position on an echo-bed with an ergometer. Exercise started at 50 watts and was increased in 25-watt every 3 minutes and to a maximum of 150 watts. Two-dimensional echocardiographic images were digitized and assigned in a quad-screen format for nonbiased interpretation. Total wall motion score (TWMS) was the sum of the wall motion score, from normokinesis (0) to dyskinesis (4), of 16 segments. Image quality score index (IQSI) was the mean of the image quality scores in all views. All of the patients underwent coronary arteriography. Significant coronary stenosis was defined as≧75% stenosis of the large coronary arteries. Two-dimensional echocardiographic studies were adequate for analysis in 133 patients during the peak-exercise stage (peak-exercise) and in 137 patients 30 to 60 seconds after the end of exercise (post-exercise). TWMS at peak-exercise was higher than at post-exercise, while IQSI at peak-exercise was lower than at post-exercise. Sensitivity at peak-exercise versus that at post-exercise was 91% versus 79% (p<0.05); specificity, 76% versus 85%; and diagnostic accuracy, 88% versus 80% (p<0.05), respectively. We conclude that despite poor image quality on exercise echocardiography, better diagnostic accuracy was attained by assessing wall motion changes at peak-exercise than at post-exercise.