Deep Learning for Ultrasound Beamforming in Flexible Array Transducer.
Deep Learning for Ultrasound Beamforming in Flexible Array Transducer.
复制标题
柔性阵列换能器中超声波束形成的深度学习。
DOI：
10.1109/tmi.2021.3087450
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发表时间：
2021-11
影响因子：
10.6
通讯作者：
Ding K
中科院分区：
文献类型：
--
作者：
Huang X;Lediju Bell MA;Ding K
超声成像已被开发用于肿瘤跟踪的图像引导放射治疗，而柔性阵列传感器是完成这项任务的有前途的工具。它可以减少传统超声换能器对用户的依赖和造成的解剖变化。然而，由于其灵活的几何形状，传统的延迟和求和（DAS）波束形成器可能会应用不正确的时间延迟的射频（RF）数据和产生的B模式图像具有相当大的散焦和失真。为了解决这个问题，我们提出了一种新的端到端深度学习方法，当换能器几何形状未知时，该方法可以替代传统的DAS波束形成器。设计了不同的深度神经网络（DNN）来学习每个通道的适当时间延迟，并期望它们直接从RF通道数据重建未失真的高质量B模式图像。我们比较了DNN的结果，标准DAS波束形成的结果，使用模拟和灵活的阵列换能器扫描数据。利用所提出的DNN方法，点散射的平均半高宽（FWHM）在仿真和扫描结果中分别降低了1.80 mm和1.31 mm;无回声囊肿在仿真和体模扫描中的对比噪声比（CNR）分别提高了0.79 dB和1.69 dB;所有囊肿的纵横比接近1。实验结果表明，该方法能有效地减小失真，提高重建图像的横向分辨率和对比度。
Ultrasound imaging has been developed for image-guided radiotherapy for tumor tracking, and the flexible array transducer is a promising tool for this task. It can reduce the user dependence and anatomical changes caused by the traditional ultrasound transducer. However, due to its flexible geometry, the conventional delay-and-sum (DAS) beamformer may apply incorrect time delay to the radio-frequency (RF) data and produce B-mode images with considerable defocusing and distortion. To address this problem, we propose a novel end-to-end deep learning approach that may alternate the conventional DAS beamformer when the transducer geometry is unknown. Different deep neural networks (DNNs) were designed to learn the proper time delays for each channel, and they were expected to reconstruct the undistorted high-quality B-mode images directly from RF channel data. We compared the DNN results to the standard DAS beamformed results using simulation and flexible array transducer scan data. With the proposed DNN approach, the averaged full-width-at-half-maximum (FWHM) of point scatters is 1.80 mm and 1.31 mm lower in simulation and scan results, respectively; the contrast-to-noise ratio (CNR) of the anechoic cyst in simulation and phantom scan is improved by 0.79 dB and 1.69 dB, respectively; and the aspect ratios of all the cysts are closer to 1. The evaluation results show that the proposed approach can effectively reduce the distortion and improve the lateral resolution and contrast of the reconstructed B-mode images.