BAYESIAN ENHANCEMENT OF ECHOCARDIOGRAPHIC STRAIN ASSESSMENT

描述（申请人提供）：评价节段性心肌壁增厚一直是超声心动图成像的主要工具。近年来，在超声心动图实验室对全局和局部心肌应变的定量评价已被证明是可行的。尽管这种基于菌株的评估具有潜力，但临床医生经常发现分析数据所需的时间令人望而却步，而且数据量太大，难以处理，无法进行常规临床使用。应变和应变率随时间数据的分析是非常具有挑战性的，因为通常较差的信噪比和分析数据所需的大量工作。该研究引入了贝叶斯方法进行模型选择和参数估计，从而提高了自动数据缩减和报告的质量，旨在克服目前限制充分使用超声心动图衍生的基于应变的数据的障碍。所提出的贝叶斯方法的价值在于，向医生提供了生理上有意义的结果的简明总结（如最大应变率、到最大应变的时间等），以及显著改善的应变率与时间曲线，从这些曲线中可以得到有意义的解释。为了发展提出的贝叶斯方法来增强基于应变的测量，我们确定了以下具体目标：#1)实现基于贝叶斯概率的方法来建模应变和应变率曲线，以便简化这些数据中的特定特征的分析，解释和识别，减少时间密集，并且受异常噪声的影响较小；#2)通过处理由50名成人和40名儿童受试者组成的现有数据集的应变数据，以确定生理相关参数，证明基于贝叶斯的数据约简在临床环境中的适用性。该研究的长期目标是通过提高测量值的稳定性和可靠性来简化这种方法，从而使其在临床环境中作为诊断工具更常规地使用。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.