Development of deep learning-based holographic ultrasound generation algorithm

IF 0.2 Q4 ACOUSTICS
Moon Hwan Lee and Jae Youn Hwang
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引用次数: 0

Abstract

Recently, an ultrasound hologram and its applications have gained attention in the ultrasound research field. However, the determination technique of transmit signal phases, which generate a hologram, has not been significantly advanced from the previous algorithms which are time-consuming iterative methods. Thus, we applied the deep learning technique, which has been previously adopted to generate an optical hologram, to generate an ultrasound hologram. We further examined the Deep learning-based Holographic Ultrasound Generation algorithm (Deep-HUG). We implement the U-Net-based algorithm and examine its generalizability by training on a dataset, which consists of randomly distributed disks, and testing on the alphabets (A-Z). Furthermore, we compare the Deep-HUG with the previous algorithm in terms of computation time, accuracy, and uniformity. It was found that the accuracy and uniformity of the Deep-HUG are somewhat lower than those of the previous algorithm whereas the computation time is 190 times faster than that of the previous algorithm, demonstrating that Deep-HUG has potential as a useful technique to rapidly generate an ultrasound hologram for various applications.
基于深度学习的全息超声生成算法的开发
近年来,超声全息图及其应用在超声研究领域引起了人们的关注。然而,产生全息图的发射信号相位的确定技术与以前的算法相比并没有显著进步,这些算法是耗时的迭代方法。因此,我们应用了以前用于生成光学全息图的深度学习技术来生成超声全息图。我们进一步研究了基于深度学习的全息超声生成算法(Deep-HUG)。我们实现了基于U-Net的算法,并通过在由随机分布的磁盘组成的数据集上进行训练和在字母表(a-Z)上进行测试来检验其可推广性。此外,我们还将Deep HUG与以前的算法在计算时间、精度和一致性方面进行了比较。研究发现,Deep HUG的精度和均匀性略低于先前算法的精度和一致性,而计算时间比先前算法快190倍,这表明Deep HUG作为一种用于各种应用的快速生成超声全息图的有用技术具有潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.60
自引率
50.00%
发文量
1
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