Background

Conventional ultrasound (US) provides important qualitative information, although there is a need to evaluate the influence of the input parameters on the output signal and standardise the acquisition for an adequate quantitative perfusion assessment. The present study analyses how the variation in the input parameters influences the measurement of the perfusion parameters.

Methods

A software tool with simulator of the conventional US signal was created, and the influence of the different input variables on the derived biomarkers was analysed by varying the image acquisition configuration. The input parameters considered were the dynamic range, gain, and frequency of the transducer. Their influence on mean transit time (MTT), the area under the curve (AUC), maximum intensity (MI), and time to peak (TTP) parameters as outputs of the quantitative perfusion analysis was evaluated. A group of 13 patients with hepatocarcinoma was analysed with both a commercial tool and an in-house developed software.

Results

The optimal calculated inputs which minimise errors while preserving images’ readability consisted of gain of 15 dB, dynamic range of 60 dB, and frequency of 1.5 MHz. The comparison between the in-house developed software and the commercial software provided different values for MTT and AUC, while MI and TTP were highly similar.

Conclusion

Input parameter selection introduces variability and errors in US perfusion parameter estimation. Our results may add relevant insight into the current knowledge of conventional US perfusion and its use in lesions characterisation, playing in favour of optimised standardised parameter configuration to minimise variability.

中文翻译：

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通过数字参考对象和分析工具优化超声肝灌注。

背景

常规超声（US）提供重要的定性信息，尽管需要评估输入参数对输出信号的影响并标准化采集以进行足够的定量灌注评估。本研究分析了输入参数的变化如何影响灌注参数的测量。

方法

创建了具有常规US信号模拟器的软件工具，并通过更改图像采集配置来分析了不同输入变量对衍生生物标记的影响。考虑的输入参数是传感器的动态范围，增益和频率。评估了它们对平均灌注时间（MTT），曲线下面积（AUC），最大强度（MI）和峰时间（TTP）参数的影响，这些参数是定量灌注分析的输出。使用商业工具和内部开发的软件对13例肝癌患者进行了分析。

结果

在保持图像可读性的同时，将误差最小化的最佳计算输入包括增益15 dB，动态范围60 dB和频率1.5 MHz。内部开发的软件和商业软件之间的比较为MTT和AUC提供了不同的值，而MI和TTP则非常相似。

结论

输入参数选择在US灌注参数估计中引入了可变性和误差。我们的结果可能会增加对常规美国灌注及其在病灶表征中使用的最新知识的相关见解，从而有助于优化标准化参数配置以最大程度地减少变异性。