基于深度学习的多任务超声波束形成

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-10-23 DOI:10.3390/info14100582
Elay Dahan, Israel Cohen
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引用次数: 0

摘要

本文提出了一种应用于超声波束形成的多任务学习新方法。波束形成是超声图像形成管道的关键组成部分。超声波图像是使用来自多个传感器元件的传感器读数构建的,每个元件通常每帧捕获多个采集。因此,波束形成器对帧率性能和整体图像质量至关重要。此外,通常对波束形成的图像进行后处理,如图像去噪,以达到高清晰度的诊断。这项工作展示了一个全卷积神经网络,它可以通过应用一种新的权值归一化方案来学习不同的任务。我们通过拟合子采样任务的权值归一化参数来适应高帧率要求,并通过优化散斑减少任务的归一化参数来适应图像去噪。我们的模型在像素级措施上优于单角度延迟和求和,用于散斑噪声降低、子采样和单角度重建。
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Deep-Learning-Based Multitask Ultrasound Beamforming
In this paper, we present a new method for multitask learning applied to ultrasound beamforming. Beamforming is a critical component in the ultrasound image formation pipeline. Ultrasound images are constructed using sensor readings from multiple transducer elements, with each element typically capturing multiple acquisitions per frame. Hence, the beamformer is crucial for framerate performance and overall image quality. Furthermore, post-processing, such as image denoising, is usually applied to the beamformed image to achieve high clarity for diagnosis. This work shows a fully convolutional neural network that can learn different tasks by applying a new weight normalization scheme. We adapt our model to both high frame rate requirements by fitting weight normalization parameters for the sub-sampling task and image denoising by optimizing the normalization parameters for the speckle reduction task. Our model outperforms single-angle delay and sum on pixel-level measures for speckle noise reduction, subsampling, and single-angle reconstruction.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
期刊最新文献
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