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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >A novel transcranial ultrasound imaging method with diverging wave transmission and deep learning approach
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A novel transcranial ultrasound imaging method with diverging wave transmission and deep learning approach

机译：具有发散波传输和深度学习方法的新型经颅超声成像方法

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摘要

Real time brain transcranial ultrasound imaging is extremely intriguing because of its numerous applications. However, the skull causes phase distortion and amplitude attenuation of ultrasound signals due to its density: the speed of sound is significantly different in bone tissue than in soft tissue. In this study, we propose an ultrafast transcranial ultrasound imaging technique with diverging wave (DW) transmission and a deep learning approach to achieve large field-of-view with high resolution and real time brain ultrasound imaging. DW transmission provides a frame rate of several kiloHz and a large field of view that is suitable for human brain imaging via a small acoustic window. However, it suffers from poor image quality because the diverging waves are all unfocused. Here, we adopted adaptive beamforming algorithms to improve both the image contrast and the lateral resolution. Both simulated and in situ experiments with a human skull resulted in significant image improvements. However, the skull still introduces a wavefront offset and distortion, which degrades the image quality even when adaptive beamforming methods are used. Thus, we also employed a U-Net neural network to detect the contour and position of the skull directly from the acquired RF signal matrix. This approach avoids the need for beamforming, image reconstruction, and image segmentation, making it more suitable for clinical use. (C) 2020 Elsevier B.V. All rights reserved.
机译：实时脑经颅超声成像由于其许多应用而极为困难。然而，由于其密度，颅骨导致超声信号的相失真和超声信号的幅度衰减：骨组织中的声速显着不同于软组织。在这项研究中，我们提出了一种超快经颅超声成像技术，具有发散波（DW）传输和深度学习方法，以实现高分辨率和实时脑超声成像的大视野。 DW传输提供了几千升的帧速率和大视野，适用于通过小型声窗口的人脑成像。然而，由于不同的波浪都不聚焦，它受到差的图像质量。这里，我们采用自适应波束成形算法来改善图像对比度和横向分辨率。两者都与人类头骨模拟和原位实验导致显着的图像改善。然而，颅骨仍然引入波前偏移和失真，即使使用自适应波束成形方法，也会降低图像质量。因此，我们还采用了U-Net神经网络直接从所获取的RF信号矩阵检测颅骨的轮廓和位置。这种方法避免了对波束成形，图像重建和图像分割的需要，使其更适合临床使用。 （c）2020 Elsevier B.V.保留所有权利。

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