Department of Electrical and Computer Engineering, University of Western Ontario, London, Ontario, Canada.
IEEE Trans Ultrason Ferroelectr Freq Control. 2010 Dec;57(12):2627-36. doi: 10.1109/TUFFC.2010.1737.
Two methods for simulation of ultrasound wavefront distortion are introduced and compared with aberration produced in simulations using digitized breast tissue specimens and a conventional multiple time-shift screen model. In the first method, aberrators are generated using a computational model of breast anatomy. In the second method, 10 to 12 irregularly shaped, strongly scattering inclusions are superimposed on the multiple-screen model to create a screen-inclusion model. Linear 2-D propagation of a 7.5-MHz planar, pulsed wavefront through each aberrator is computed using a first-order k-space method. The anatomical and screen-inclusion models reproduce two characteristics of arrival-time fluctuations observed in simulations using the digitized specimens that are not represented in simulations using the multiple-screen model: non-Gaussian first-order statistics and sharp changes in the rms arrival-time fluctuation as a function of propagation distance. The anatomical and screen-inclusion models both produce energy- level fluctuations similar to the digitized specimens, but the anatomical model more closely matches the pulse-shape distortion produced by the specimens. Both aberration models can readily be extended to 3-D, and the screen-inclusion model has the advantage of simplicity of implementation. Both models should enable more rigorous evaluation of adaptive focusing algorithms than is possible using conventional time-shift screen models.
介绍了两种模拟超声波波前失真的方法，并将其与使用数字化乳腺组织标本和传统的多次时移屏模型进行模拟产生的像差进行了比较。在第一种方法中，使用乳腺解剖学的计算模型来产生像差。在第二种方法中，在多次时移屏模型上叠加 10 到 12 个不规则形状、强散射的内含物，以创建一个屏-内含物模型。使用一阶 k 空间方法计算线性 2-D 传播的 7.5-MHz 平面、脉冲波前通过每个像差的传播。解剖模型和屏-内含物模型再现了使用数字化标本进行模拟中观察到的到达时间波动的两个特征，而这些特征在使用多次时移屏模型进行模拟中没有体现：非高斯一阶统计和均方根到达时间波动随传播距离的急剧变化。解剖模型和屏-内含物模型都产生与数字化标本相似的能量级波动，但解剖模型更接近标本产生的脉冲形状失真。这两种像差模型都可以很容易地扩展到 3-D，而屏-内含物模型具有实现简单的优点。这两种模型都应该能够比使用传统的时移屏模型更严格地评估自适应聚焦算法。
