首页> 外文会议>IEEE International Ultrasonics Symposium >Evaluation of Contrast to Noise Ratio of Parametric Images of Regularized Estimates of Quantitative Ultrasound
【24h】

Evaluation of Contrast to Noise Ratio of Parametric Images of Regularized Estimates of Quantitative Ultrasound

机译:定量超声正则估计的参量图像对比度噪声比评估

获取原文
获取外文期刊封面目录资料

摘要

The granular appearance of B-mode image emanates from scattering particles, rendering visual assessment of B-mode image difficult. However, the analysis of echo signals that produce B-mode image speckle can disclose important information about the physical characteristic of the tissue. Quantitative ultrasound investigates the frequency content of backscattered echo signals to provide estimates of acoustic properties of tissue. Regularized estimation leads to more accurate and precise estimates of such acoustic properties. In this study, we investigate whether parametric images of regularized estimates of the acoustic concentration can provide better conspicuity of high contrast objects than conventional B-mode images. To this end, we apply regularized estimation of acoustic concentration using Dynamic Programming (DP) to data acquired from a Gammex 410SCG phantom. The phantom contains three inclusions with different echogenicities, which are created from different concentration of scatterers. Conspicuity is quantified in terms of the inclusion contrast to noise ratio (CNR) and border resolution. Our results demonstrate that using regularized QUS, the CNR of parametric images of the acoustic concentration was substantially higher CNR than those of conventional B-mode images, at the expense of border resolution.
机译:B模式图像的颗粒状外观来自散射粒子,因此很难对B模式图像进行视觉评估。但是,对产生B模式图像斑点的回声信号的分析可以揭示有关组织物理特征的重要信息。定量超声研究反向散射回波信号的频率内容,以提供组织声学特性的估计值。正规估计导致对这种声学特性的更准确和精确的估计。在这项研究中,我们调查了常规的声波浓度估计参数化图像是否可以比传统的B模式图像提供更好的高对比度对象的醒目性。为此,我们使用动态编程(DP)对从Gammex 410SCG体模获取的数据应用规范化的声波集中度估计。体模包含三个具有不同回声性的内含物,这些内含物是由不同浓度的散射体产生的。显眼性是根据包含比与噪声比(CNR)和边界分辨率进行量化的。我们的结果表明,使用正则化QUS,声集中参数图像的CNR明显高于常规B模式图像的CNR,但会降低边界分辨率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号