Evaluation of segmental myocardial wall thickening has been a primary tool of echocardiographic imaging. Recently, quantitative evaluation of global and regional myocardial strain has been shown to be feasible in the echocardiographic laboratory. In spite of the potential for such strain-based evaluation, clinicians frequently find the time required to analyze the data to be prohibitive and the amount of data to be far too large and unwieldy to permit routine clinical use. Analyses of the strain and strain-rate versus time data are very challenging because of typically poor signal-to-noise ratios and the significant effort required analyzing the data. The proposed research, which introduces Bayesian methods for model selection and parameter estimation coupled with resulting improved quality of automated data reduction and reporting, is designed as an approach for overcoming the obstacles that currently limit the full use of echocardiographically derived strain-based data. The value of the proposed Bayesian approach arises because the physician is presented with a concise summary of physiologically meaningful results (such as maximum strain rate, time to maximum strain, etc.) as well as significantly improved strain rate versus time curves from which meaningful interpretations are possible. To develop the proposed Bayesian approach of enhancing strain-based measurements, we have identified the following Specific Aims: #1) Implementation of Bayesian probability based methods for modeling strain and strain rate curves such that analysis, interpretation, and identification of specific features in these data are simplified, less time-intensive, and less affected by anomalous noise;and #2) Demonstration of the applicability of Bayesian-based data reduction in the clinical setting by processing strain data from existing data sets consisting of 50 adult and 40 pediatric subjects for the determination of physiologically relevant parameters. The long-term goal of the proposed research is to streamline this approach by improving the stability and reliability of the measured values, resulting in their more routine use as a diagnostic tool in the clinical setting.
Cardiac disease reduces quality of life and carries enormous health care costs. The proposed research uses novel Bayesian methods for improving the stability and reliability of myocardial strain measurements resulting in their more routine use as a diagnostic tool in the clinical setting. These fundamental improvements are designed to identify cardiac abnormalities more reliably, thus enhancing patient care.
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