Ultrasound imaging with flexible transducers based on real-time and high-accuracy shape estimation.

IF 3.8 2区 物理与天体物理 Q1 ACOUSTICS Ultrasonics Pub Date : 2024-12-14 DOI:10.1016/j.ultras.2024.107551
Xue Gao, Lihong Huang, Peng Huang, Yuanyuan Wang, Yi Guo
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Abstract

Ultrasound imaging with flexible transducers requires the knowledge of shape geometry for effective beamforming, which such geometry is variable and often unknown. The conventional iteration-based shape estimation methods estimate transducer shape with high computational expense. Although deep-learning-based methods are introduced to reduce computation time, their low shape estimation accuracy limits the practical applications. In this paper, we propose a novel deep-learning-based approach, called FlexSANet, for shape estimation in ultrasound imaging with flexible transducers, which rapidly achieves precise shape estimation and then reconstructs high-quality images. First, in-phase/quadrature (I/Q) data are demodulated from raw radio frequency (RF) data to provide comprehensive guidance for the estimation task. A sparse processing mechanism is employed to extract crucial channel signals, resulting in sparse I/Q data and reducing the estimation time. Then, a spatial-aware shape estimation network establishes a one-shot mapping between the sparse I/Q data and the flexible probe shape. Finally, the ultrasound image is reconstructed using the delay-and-sum (DAS) beamformer with estimated shape. Massive comparisons on simulation datasets and in vivo datasets demonstrate the superiority of the proposed shape estimation method in rapidly and accurately estimating the transducer shape, leading to real-time and high-quality imaging. The mean absolute error of element position in shape estimation is below 1/8 wavelengths for simulation and in vivo experiments, indicating minimal element position error. The structural similarity between the ultrasound images reconstructed with real and estimated shapes is above 0.84 for simulation experiments and 0.80 for in vivo experiments, demonstrating superior image quality. More significantly, its estimation time on CPU of only 0.12 s promises clinical application potential of flexible ultrasound transducers.

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基于实时、高精度形状估计的柔性换能器超声成像。
利用柔性换能器进行超声成像需要了解有效波束形成的几何形状,而这种几何形状是可变的,通常是未知的。传统的基于迭代的形状估计方法计算量大。虽然引入了基于深度学习的方法来减少计算时间,但其较低的形状估计精度限制了实际应用。在本文中,我们提出了一种新的基于深度学习的方法,称为FlexSANet,用于柔性换能器超声成像中的形状估计,该方法可以快速实现精确的形状估计,然后重建高质量的图像。首先,从原始射频(RF)数据中解调同相/正交(I/Q)数据,为估计任务提供全面指导。采用稀疏处理机制提取关键信道信号,得到稀疏的I/Q数据,减少估计时间。然后,利用空间感知形状估计网络在稀疏I/Q数据和柔性探针形状之间建立一次映射。最后,利用估计形状的延迟和波束形成器重建超声图像。仿真数据集和体内数据集的大量比较表明,所提出的形状估计方法在快速准确地估计换能器形状方面具有优势,从而实现实时和高质量的成像。在模拟和体内实验中,形状估计中元素位置的平均绝对误差在1/8波长以下,表明元素位置误差最小。用真实形状重建的超声图像与估计形状重建的超声图像的结构相似度在模拟实验的0.84以上,在体内实验的0.80以上,显示出较好的图像质量。更重要的是,其对CPU的估计时间仅为0.12 s,为柔性超声换能器的临床应用提供了前景。
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
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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