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arXiv 2204.04537physics.med-phcs.LG
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基于深度学习的单通道超声RF信号超分辨微泡定位

Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning

University of Twente(特文特大学);INRIA(法国国家信息与自动化研究所)

Nathan Blanken, Jelmer M. Wolterink, Hervé Delingette, Christoph Brune, Michel Versluis, Guillaume Lajoinie

摘要:

近年来,超声定位显微镜(ULM)的超分辨率超声成像受到了广泛关注。然而,ULM依赖于血管中低浓度的微泡,最终导致长的采集时间。在这里,我们提出了一种替代的超分辨率方法,基于直接反卷积的单通道超声射频(RF)信号与一维扩张卷积神经网络(CNN)。这项工作的重点是低频超声(1.7 MHz)的单分散微泡(测量体积中高达1000个微泡,对应于94%的平均回波重叠)的密集云的深度成像(10 cm)。数据是用模拟器产生的,该模拟器使用大范围的声压(5-250 kPa),并捕获共振的脂质涂层微泡的完整的非线性响应。该网络是用一种新的双损失函数训练的,该函数具有分类损失和回归损失的元素,并提高了输出的检测定位特性。而施加定位容差为0会产生较差的检测度量,施加对应于波长的4%的定位容差会产生0.90的精确度和召回率。此外,检测随着声压的增加而改善,并且随着微泡密度的增加而恶化。所提出的方法的超分辨率超声成像的潜力证明了延迟和总和重建与反卷积的元素数据。所得到的图像显示了一个数量级的轴向分辨率的增益相比,未处理的元素数据的延迟和总和重建。

英文摘要:

Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles in the blood vessels, ultimately resulting in long acquisition times. Here, we present an alternative super-resolution approach, based on direct deconvolution of single-channel ultrasound radio-frequency (RF) signals with a one-dimensional dilated convolutional neural network (CNN). This work focuses on low-frequency ultrasound (1.7 MHz) for deep imaging (10 cm) of a dense cloud of monodisperse microbubbles (up to 1000 microbubbles in the measurement volume, corresponding to an average echo overlap of 94%). Data are generated with a simulator that uses a large range of acoustic pressures (5-250 kPa) and captures the full, nonlinear response of resonant, lipid-coated microbubbles. The network is trained with a novel dual-loss function, which features elements of both a classification loss and a regression loss and improves the detection-localization characteristics of the output. Whereas imposing a localization tolerance of 0 yields poor detection metrics, imposing a localization tolerance corresponding to 4% of the wavelength yields a precision and recall of both 0.90. Furthermore, the detection improves with increasing acoustic pressure and deteriorates with increasing microbubble density. The potential of the presented approach to super-resolution ultrasound imaging is demonstrated with a delay-and-sum reconstruction with deconvolved element data. The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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