In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can produce arbitrarily high CNR values with no real effect on the probability of lesion detection. We generalize the definition of CNR based on the overlap area between two probability density functions. This generalized CNR (gCNR) is robust against dynamic range alterations; it can be applied to all kind of images, units, or scales; it provides a quantitative measure for contrast; and it has a simple statistical interpretation, i.e., the success rate that can be expected from an ideal observer at the task of separating pixels. We test gCNR on several state-of-the-art imaging algorithms and, in addition, on a trivial compression of the dynamic range. We observe that CNR varies greatly between the state-of-the-art methods, with improvements larger than 100%. We observe that trivial compression leads to a CNR improvement of over 200%. The proposed index, however, yields the same value for compressed and uncompressed images. The tested methods showed mismatched performance in terms of lesion detectability, with variations in gCNR ranging from −0.08 to +0.29. This new metric fixes a methodological flaw in the way we study contrast and allows us to assess the relevance of new imaging algorithms.

中文翻译：

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广义对比度噪声比：病变可检测性的正式定义。

在过去的 30 年中，对比度噪声比 (CNR) 已被用来估计超声图像中的对比度和病变可检测性。最近的研究表明，CNR 不能与现代波束形成器一起使用，因为动态范围改变可以产生任意高的 CNR 值，而对病变检测的概率没有实际影响。我们根据两个概率密度函数之间的重叠面积概括了 CNR 的定义。这种广义 CNR (gCNR) 对于动态范围变化具有鲁棒性；它可以应用于所有类型的图像、单位或尺度；它提供了对比度的定量测量；它有一个简单的统计解释，即理想观察者在分离像素任务中可以预期的成功率。我们在几种最先进的成像算法以及动态范围的简单压缩上测试了 gCNR。我们观察到，最先进的方法之间的 CNR 差异很大，改进幅度超过 100%。我们观察到，微不足道的压缩可以使 CNR 提高 200% 以上。然而，所提出的索引对于压缩和未压缩图像产生相同的值。测试方法在病变可检测性方面表现出不匹配的性能，gCNR 的变化范围为 -0.08 至 +0.29。这个新指标修复了我们研究对比度的方法缺陷，使我们能够评估新成像算法的相关性。