A Deep-Learning Approach for Non-Invasive Temperature Measurements Using Ultrasound Images

Y. Iseki, Tsugumi Nishidate
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引用次数: 1

Abstract

This paper proposes a deep-learning temperature measurement method using ultrasound images that is based on the thermal dependance of local changes in the speed of sound. In this method, the temperature distribution is measured using a non-invasive image analysis technique. In a previous study, we found a temperature measurement accuracy of 1.0 °C or less. However, our previous method has some disadvantages. First, the image analysis parameters (e.g., the size of the template and the cross-correlation threshold) are empirically determined. Second, it is necessary to obtain the thermal constant ktissue according to the type of tissue and the analysis parameters. To overcome these problems, we propose a new method using deep-learning. This new method is divided into three steps. The first step is to determine the image analysis parameters from the ultrasound images using a convolutional neural network (CNN). The second step is to analyze the image using the estimated analysis parameters to obtain a normalized temperature distribution. The third step is to determine the thermal constant ktissue to calibrate the temperature increase using multi-layered perceptron (MLP). In this paper, first, we propose three types of image fusion methods to input the ultrasound images into the CNN. Comparing the results of the three methods, we determine the optimal CNN structure. Second, we determine the optimal MLP structure by changing the number of hidden layers and neurons. Finally, as described above, we obtain the temperature distribution. Our results indicate that the proposed deep-learning method can effectively provide non-invasive temperature measurements.
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一种利用超声图像进行无创温度测量的深度学习方法
本文提出了一种基于声速局部变化的热依赖性的超声图像深度学习温度测量方法。在该方法中,使用非侵入式图像分析技术测量温度分布。在之前的研究中,我们发现温度测量精度为1.0°C或更低。然而,我们之前的方法有一些缺点。首先,根据经验确定图像分析参数(如模板的大小和互相关阈值)。其次,根据组织类型和分析参数,需要得到组织的热常数k。为了克服这些问题,我们提出了一种利用深度学习的新方法。这种新方法分为三步。第一步是使用卷积神经网络(CNN)从超声图像中确定图像分析参数。第二步是利用估计的分析参数对图像进行分析,得到归一化的温度分布。第三步是使用多层感知器(MLP)确定热常数k组织来校准温度升高。本文首先提出了三种图像融合方法,将超声图像输入到CNN中。比较了三种方法的结果,确定了最优的CNN结构。其次,我们通过改变隐藏层和神经元的数量来确定最优MLP结构。最后,如上所述,我们得到了温度分布。我们的研究结果表明,所提出的深度学习方法可以有效地提供无创温度测量。
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