LOW-CONTRAST DETECTABILITY OF ELASTOGRAPHY VS SONOGRAPHY

弹性成像是一种很有前途的新成像技术，它可以提供诊断 常规超声检查不能提供的信息。形象 形成过程可能很复杂；尤其是 弹性图与应变测量的准确度和精密度 强烈依赖于大量的数据采集和信号处理 参数。这个项目的目标是提供客观的 关于弹性图质量的评定标准 超声图，以指导诊断和治疗方法的发展 优化特定成像任务的诊断性能。 本项目重点研究弹性成像的统计特性，其中包括 测量圆环低对比度可探测性的具体目的 噪声限制条件下的目标。关于应变估计的分析 方法提供的概率模型准确地表示 弹性成像图像数据。从概率模型来看，似然比 以发现目标的理想观测器的策略 并测量相应的信噪比(SNR)。 计划进行观察者性能测量，以测量以下各项的信噪比 专业的人类观察者。根据SNR测量结果，我们计算出视觉 弹性成像的检测效率。检测效率是 我们提出的优化信号处理方法的评估标准， 评估有关组织如何响应的假设的准确性 施加应力，并将低对比度可检测性与 超声波检查。项目3提供了一种评估组织运动的方法 项目1中开发的模型和开发的信号处理方法 在项目2中。弹性成像图像的统计分析和 该项目的SNR分析方面是独立的。

Elastography is a promising new imaging technique that provides diagnostic information not available with conventional ultrasonography. The image formation process can be complex; in particular, the appearance of the elastogram and the accuracy and precision of the strain measurement strongly depend on the many data acquisition and signal processing parameters. The objective of this project is to provide objective criteria for assessing the quality of elastograms with respect to sonograms to guide development of the modality for diagnosis and to optimize diagnostic performance for specific imaging tasks. This project focuses on the statistical properties of elastography, with the specific aim of measuring the low-contrast detectability of circular targets under noise-limited conditions. Analysis of the strain estimation method provides the probability models that accurately represent elastographic image data. From the probability models, likelihood ratios are derived to discover the strategy of the ideal observer for target detection and to measure the corresponding signal-to-noise ratio (SNR). Observer performance measurements are planned to measure the SNR for expert human observers. From the SNR measurements we compute visual detection efficiencies for elastography. Detection efficiency is the evaluation criterion we propose for optimizing signal processing methods, assessing the accuracy of assumptions regarding how tissues respond to an applied stress, and comparing low-contrast detectability with that of sonography. Project 3 offers a means for evaluating the tissue motion models developed in Project 1 and the signal processing methods developed in Project 2. The statistical analysis of elastographic images and the SNR analysis aspects of the project stand alone.