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超声矩阵成像 - 第二部分:用于多个等晕区内像差校正的畸变矩阵

Ultrasound Matrix Imaging-Part II: The Distortion Matrix for Aberration Correction Over Multiple Isoplanatic Patches.

作者信息

Lambert William, Cobus Laura A, Robin Justine, Fink Mathias, Aubry Alexandre

出版信息

IEEE Trans Med Imaging. 2022 Dec;41(12):3921-3938. doi: 10.1109/TMI.2022.3199483. Epub 2022 Dec 2.

Abstract

This is the second article in a series of two which report on a matrix approach for ultrasound imaging in heterogeneous media. This article describes the quantification and correction of aberration, i.e. the distortion of an image caused by spatial variations in the medium speed-of-sound. Adaptive focusing can compensate for aberration, but is only effective over a restricted area called the isoplanatic patch. Here, we use an experimentally-recorded matrix of reflected acoustic signals to synthesize a set of virtual transducers. We then examine wave propagation between these virtual transducers and an arbitrary correction plane. Such wave-fronts consist of two components: (i) An ideal geometric wave-front linked to diffraction and the input focusing point, and; (ii) Phase distortions induced by the speed-of-sound variations. These distortions are stored in a so-called distortion matrix, the singular value decomposition of which gives access to an optimized focusing law at any point. We show that, by decoupling the aberrations undergone by the outgoing and incoming waves and applying an iterative strategy, compensation for even high-order and spatially-distributed aberrations can be achieved. After a numerical validation of the process, ultrasound matrix imaging (UMI) is applied to the in-vivo imaging of a gallbladder. A map of isoplanatic modes is retrieved and is shown to be strongly correlated with the arrangement of tissues constituting the medium. The corresponding focusing laws yield an ultrasound image with drastically improved contrast and transverse resolution. UMI thus provides a flexible and powerful route towards computational ultrasound.

摘要

这是关于非均匀介质中超声成像矩阵方法的系列两篇文章中的第二篇。本文描述了像差的量化和校正,即由介质声速的空间变化引起的图像失真。自适应聚焦可以补偿像差,但仅在称为等相面斑块的受限区域内有效。在这里,我们使用实验记录的反射声信号矩阵来合成一组虚拟换能器。然后,我们研究这些虚拟换能器与任意校正平面之间的波传播。这样的波前由两个分量组成:(i)与衍射和输入聚焦点相关的理想几何波前,以及;(ii)由声速变化引起的相位失真。这些失真存储在所谓的失真矩阵中,其奇异值分解可在任何点获得优化的聚焦定律。我们表明,通过解耦出射波和入射波所经历的像差并应用迭代策略,可以实现对甚至高阶和空间分布像差的补偿。在对该过程进行数值验证之后,超声矩阵成像(UMI)被应用于胆囊的体内成像。获取了等相面模式图,并显示出与构成介质的组织排列密切相关。相应的聚焦定律产生了对比度和横向分辨率大幅提高的超声图像。因此,UMI为计算超声提供了一条灵活而强大的途径。

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