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Combination of contrast with stress echocardiography: a practical guide to methods and interpretation.

ABSTRACT: Contrast echocardiography has an established role for enhancement of the right heart Doppler signals, the detection of intra-cardiac shunts, and most recently for left ventricular cavity opacification (LVO). The use of intravenously administered micro-bubbles to traverse the myocardial microcirculation in order to outline myocardial viability and perfusion has been the source of research studies for a number of years. Despite the enthusiasm of investigators, myocardial contrast echocardiography (MCE) has not attained routine clinical use and LV opacification during stress has been less widely adopted than the data would support. The purpose of this review is to facilitate an understanding of the involved imaging technologies that have made this technique more feasible for clinical practice, and to guide its introduction into the practice of the non-expert user.

SUBMITTER: Moir S

PROVIDER: S-EPMC516786 | biostudies-literature | 2004 Aug

REPOSITORIES: biostudies-literature

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Combination of contrast with stress echocardiography: a practical guide to methods and interpretation.

Moir Stuart S Marwick Thomas H TH

Cardiovascular ultrasound 20040826

Contrast echocardiography has an established role for enhancement of the right heart Doppler signals, the detection of intra-cardiac shunts, and most recently for left ventricular cavity opacification (LVO). The use of intravenously administered micro-bubbles to traverse the myocardial microcirculation in order to outline myocardial viability and perfusion has been the source of research studies for a number of years. Despite the enthusiasm of investigators, myocardial contrast echocardiography ...[more]

PMID: 15331015

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Project description:BackgroundContrast and non-contrast echocardiography are crucial for cardiovascular diagnoses and treatments. Correct view classification is a foundational step for the analysis of cardiac structure and function. View classification from all sequences of a patient is laborious and depends heavily on the sonographer's experience. In addition, the intra-view variability and the inter-view similarity increase the difficulty in identifying critical views in contrast and non-contrast echocardiography. This study aims to develop a deep residual convolutional neural network (CNN) to automatically identify multiple views of contrast and non-contrast echocardiography, including parasternal left ventricular short axis, apical two, three, and four-chamber views.MethodsThe study retrospectively analyzed a cohort of 855 patients who had undergone left ventricular opacification at the Department of Ultrasound Medicine, Wuhan Union Medical College Hospital from 2013 to 2021, including 70.3% men and 29.7% women aged from 41 to 62 (median age, 53). All datasets were preprocessed to remove sensitive information and 10 frames with equivalent intervals were sampled from each of the original videos. The number of frames in the training, validation, and test datasets were, respectively, 19,370, 2,370, and 2,620 from 9 views, corresponding to 688, 84, and 83 patients. We presented the CNN model to classify echocardiographic views with an initial learning rate of 0.001, and a batch size of 4 for 30 epochs. The learning rate was decayed by a factor of 0.9 per epoch.ResultsOn the test dataset, the overall classification accuracy is 99.1 and 99.5% for contrast and non-contrast echocardiographic views. The average precision, recall, specificity, and F1 score are 96.9, 96.9, 100, and 96.9% for the 9 echocardiographic views.ConclusionsThis study highlights the potential of CNN in the view classification of echocardiograms with and without contrast. It shows promise in improving the workflow of clinical analysis of echocardiography.

Dataset Information

Combination of contrast with stress echocardiography: a practical guide to methods and interpretation.

Publications

Combination of contrast with stress echocardiography: a practical guide to methods and interpretation.

Similar Datasets

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