利用深度学习提高单平面波成像的图像质量。

IF 3.8 2区 物理与天体物理 Q1 ACOUSTICS
Kanta Miura , Hiromi Shidara , Takuro Ishii , Koichi Ito , Takafumi Aoki , Yoshifumi Saijo , Jun Ohmiya
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引用次数: 0

摘要

在超声图像诊断中,单平面波成像(SPWI)能以超过 1000 fps 的速度获取超声图像,已被用于观察组织细节和评估血流。SPWI 通过牺牲超声图像的空间分辨率和对比度来实现高时间分辨率。为了提高 SPWI 的空间分辨率和对比度,采用了相干平面波复合(CPWC)技术,通过将从不同方向发射平面波获得的射频(RF)信号相干相加,获得高质量的超声图像,即复合图像。虽然 CPWC 能生成高质量的超声图像,但其时间分辨率低于 SPWI。为了解决这个问题,有人提出了一些方法,利用从不同方向发射少量平面波获得的射频信号重建与复合图像相当的超声图像。这些方法没有充分考虑射频信号的特性,导致图像质量低于复合图像。在本文中,我们提出了通过考虑单个平面波的射频信号特性来重建 SPWI 中高质量超声波图像的方法,以获得与 CPWC 图像质量相当的超声波图像。所提出的方法采用一维 U-网络、二维 U-网络的编码器-解码器模型及其组合,通过最小化损耗生成高质量超声图像,其中考虑了训练中平面波的点扩散效应和射频信号的频谱。我们还创建了一个公开的大规模 SPWI/CPWC 数据集,用于开发和评估深度学习方法。通过使用公共数据集和我们的数据集进行一系列实验,我们证明了与传统方法相比,我们提出的方法可以从 SPWI 中的射频信号重建更高质量的超声图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image quality improvement in single plane-wave imaging using deep learning
In ultrasound image diagnosis, single plane-wave imaging (SPWI), which can acquire ultrasound images at more than 1000 fps, has been used to observe detailed tissue and evaluate blood flow. SPWI achieves high temporal resolution by sacrificing the spatial resolution and contrast of ultrasound images. To improve spatial resolution and contrast in SPWI, coherent plane-wave compounding (CPWC) is used to obtain high-quality ultrasound images, i.e., compound images, by coherent addition of radio frequency (RF) signals acquired by transmitting plane waves in different directions. Although CPWC produces high-quality ultrasound images, their temporal resolution is lower than that of SPWI. To address this problem, some methods have been proposed to reconstruct a ultrasound image comparable to a compound image from RF signals obtained by transmitting a small number of plane waves in different directions. These methods do not fully consider the properties of RF signals, resulting in lower image quality compared to a compound image. In this paper, we propose methods to reconstruct high-quality ultrasound images in SPWI by considering the characteristics of RF signal of a single plane wave to obtain ultrasound images with image quality comparable to CPWC. The proposed methods employ encoder–decoder models of 1D U-Net, 2D U-Net, and their combination to generate the high-quality ultrasound images by minimizing the loss that considers the point spread effect of plane waves and frequency spectrum of RF signals in training. We also create a public large-scale SPWI/CPWC dataset for developing and evaluating deep-learning methods. Through a set of experiments using the public dataset and our dataset, we demonstrate that the proposed methods can reconstruct higher-quality ultrasound images from RF signals in SPWI than conventional method.
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来源期刊
Ultrasonics
Ultrasonics 医学-核医学
CiteScore
7.60
自引率
19.00%
发文量
186
审稿时长
3.9 months
期刊介绍: Ultrasonics is the only internationally established journal which covers the entire field of ultrasound research and technology and all its many applications. Ultrasonics contains a variety of sections to keep readers fully informed and up-to-date on the whole spectrum of research and development throughout the world. Ultrasonics publishes papers of exceptional quality and of relevance to both academia and industry. Manuscripts in which ultrasonics is a central issue and not simply an incidental tool or minor issue, are welcomed. As well as top quality original research papers and review articles by world renowned experts, Ultrasonics also regularly features short communications, a calendar of forthcoming events and special issues dedicated to topical subjects.
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