WO2022077866A1 - 一种基于深度学习的电阻抗成像方法 - Google Patents

一种基于深度学习的电阻抗成像方法 Download PDF

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WO2022077866A1
WO2022077866A1 PCT/CN2021/082933 CN2021082933W WO2022077866A1 WO 2022077866 A1 WO2022077866 A1 WO 2022077866A1 CN 2021082933 W CN2021082933 W CN 2021082933W WO 2022077866 A1 WO2022077866 A1 WO 2022077866A1
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deep learning
voltage data
electrical impedance
impedance imaging
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石岩
许少峰
蔡茂林
王娜
王一轩
任帅
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北京航空航天大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0536—Impedance imaging, e.g. by tomography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

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  • the invention belongs to the technical field of electrical impedance imaging, and more particularly relates to an electrical impedance imaging method based on deep learning.
  • Electrical impedance imaging technology is a new non-invasive functional detection method in recent decades, which has broad application prospects in the fields of medical imaging, geological exploration and industrial detection.
  • Image reconstruction in electrical impedance imaging is a typical nonlinear ill-conditioned inverse problem.
  • One of the traditional image reconstruction methods is to add prior information to the imaging target functional for regularization, which alleviates the ill-conditioned and underdetermined EIT inverse problem.
  • the most commonly used is Tikhonov regularization, which can obtain better reconstruction results when reconstructing images of continuous conductivity targets.
  • Tikhonov regularization which can obtain better reconstruction results when reconstructing images of continuous conductivity targets.
  • the phenomenon of excessive smoothness generally occurs, which makes the edges of the reconstructed conductivity image become blurred, and there are artifacts in the boundary between organs and organs.
  • TV Total Variation
  • the present invention provides an electrical impedance imaging method based on deep learning, which solves the problems of low reconstructed image accuracy, poor noise resistance, and low efficiency of utilizing prior knowledge in traditional electrical impedance imaging, and improves electrical impedance imaging. image reconstruction accuracy.
  • An electrical impedance imaging method based on deep learning comprising the following steps:
  • S1 Obtain the original boundary measurement voltage data set of the area to be measured, use the finite element method to calculate the initial distribution sequence of the conductivity of the area to be measured, and form a corresponding training data set after normalization;
  • step S2 Add noise of standard normal distribution to the initial distribution sequence of electrical conductivity obtained in step S1, and use a deep learning network to train a variational autoencoder using the training data set of electrical conductivity distribution to obtain prior knowledge of the EIT image;
  • step S3 use the deep learning network, measure the voltage data set according to the boundary in step S1, and use the encoder in the variational autoencoder trained in step S2 to generate hidden layer data in the network to obtain the input voltage data features;
  • step S4 According to the boundary measurement voltage data set in step S1, use the voltage data features trained in step S3 and the decoder in the variational autoencoder trained in step S2 to establish the boundary measurement voltage data set and the corresponding conductivity distribution Mapping relationship between sequences.
  • the original boundary measurement voltage data set of the area to be measured in step S1 is obtained through 16 electrode arrays.
  • the additional loss function L of the deep learning network is:
  • ⁇ is the conductivity distribution
  • ⁇ is the decoder function
  • ⁇ is the encoder function
  • D kl is the Kullback-Leibler divergence loss term
  • m is the total number of iterations
  • n is the current iteration number
  • ⁇ n is the conductivity
  • ⁇ n is the mean of ⁇
  • ⁇ n is the standard deviation of ⁇
  • N(0,1) is the standard normal distribution.
  • step S41 is further included between the step S4 and the step S5: repeating the steps S1-S4 to train the entire model.
  • the present invention proposes an electrical impedance imaging method based on deep learning, which adopts a layer-by-layer training mechanism through steps S1 to S4, firstly trains each layer of network, and then optimizes the trained network to complete the deep learning training.
  • This deep structure enables deep learning to have strong expressive ability and learning performance, and can better approximate complex functions with fewer parameters and reduce the occurrence of overfitting.
  • steps S2-S3 the features of the boundary measurement voltage data are accurately extracted, and at the same time, by dynamically adjusting the additional loss function, the prior knowledge is fully utilized, and the image reconstruction accuracy is improved.
  • the data can be reduced in dimension, the training speed is fast, and the imaging efficiency is high.
  • deep learning based on variational autoencoding can more accurately learn the characteristics of the conductivity distribution, and can better characterize the mapping relationship between the boundary measurement voltage and the conductivity distribution, and then establish the EIT deep learning network model, and then use the The model obtains the conductivity distribution corresponding to the untrained boundary measurement voltage, and completes the image reconstruction with higher speed and higher resolution.
  • the image quality can be continuously improved.
  • Figure 1 is a flow chart of the method of the present invention.
  • FIG. 2 is a structural frame diagram of a deep learning network of the present invention.
  • the present invention provides an electrical impedance imaging method based on deep learning, including the following steps:
  • S1 Obtain the original boundary measurement voltage data set of the area to be measured through 16 electrode arrays, use the finite element method to calculate the initial distribution sequence of the conductivity of the area to be measured, and form a corresponding training data set after normalization;
  • step S2 Add noise of standard normal distribution to the initial distribution sequence of conductivity obtained in step S1, and use the deep learning network to train a variational autoencoder using the training data set of conductivity distribution to obtain prior knowledge of the EIT image; image
  • the prior knowledge is the data set of the initial distribution of conductivity and the added noise of the standard normal distribution, into the loss function L, and the loss function L is used to calculate the auto-encoder function, that is, the features in the initial distribution are incorporated into the encoder function.
  • the additional loss function L of the deep learning network is:
  • ⁇ is the conductivity distribution
  • ⁇ is the decoder function: R k ⁇ R d , k ⁇ d
  • ⁇ is the encoder function: R d ⁇ R k , k ⁇ d
  • D kl is the Kullback-Leibler divergence loss term
  • m is the total number of iterations
  • n is the current iteration number
  • ⁇ n is the conductivity
  • ⁇ n is the mean of ⁇
  • ⁇ n is the standard deviation of ⁇
  • N(0,1) is the standard normal distribution.
  • step S3 use the deep learning network, measure the voltage data set according to the boundary in step S1, and use the encoder in the variational autoencoder trained in step S2 to generate hidden layer data in the network to obtain the input voltage data features;
  • step S4 According to the boundary measurement voltage data set in step S1, use the voltage data features trained in step S3 and the decoder in the variational autoencoder trained in step S2 to establish the boundary measurement voltage data set and the corresponding conductivity distribution Mapping relationship between sequences;
  • S41 Repeat steps S1-S4 to train the entire model.
  • the whole model is the mapping relationship between the boundary measurement voltage data set and the corresponding conductivity distribution sequence and the calculation steps of deep learning in the middle. These relationships and calculation steps form a large model, each time there is new voltage data data. Just run it once to get the latest conductivity, which is equivalent to training the computing network in it once. As the amount of data increases and the amount of training increases, the computational steps or the deep learning network works better.
  • step S5 According to the unprocessed boundary measurement voltage data set obtained by the 16 electrode arrays and the trained mapping relationship established in step S4, the final conductivity distribution is obtained.
  • the electrical conductivity of a certain tissue in a normal organism is determined and can be considered to be uniformly distributed within a certain range. If there is a lesion in the tissue, its electrical conductivity will be abnormal.
  • the present invention judges whether there is a disease by measuring the current conductivity distribution of a certain tissue in the organism and comparing it with the reference value of a normal healthy body.
  • the present invention proposes an electrical impedance imaging method based on deep learning, which adopts a layer-by-layer training mechanism through steps S1 to S4, firstly trains each layer of network, and then optimizes the trained network to complete the deep learning training.
  • This deep structure enables deep learning to have strong expressive ability and learning performance, and can better approximate complex functions with fewer parameters and reduce the occurrence of overfitting.
  • steps S2-S3 the features of the boundary measurement voltage data are accurately extracted, and at the same time, by dynamically adjusting the additional loss function, the prior knowledge is fully utilized, and the image reconstruction accuracy is improved.
  • the data can be reduced in dimension, the training speed is fast, and the imaging efficiency is high.
  • deep learning based on variational autoencoding can more accurately learn the characteristics of the conductivity distribution, and can better characterize the mapping relationship between the boundary measurement voltage and the conductivity distribution, and then establish the EIT deep learning network model, and then use the The model obtains the conductivity distribution corresponding to the untrained boundary measurement voltage, and completes the image reconstruction with higher speed and higher resolution.
  • the image quality can be continuously improved.

Abstract

一种基于深度学习的电阻抗成像方法，包括如下步骤：获取待测区域的原始边界测量电压数据集，算出待测区域电导率的初始分布序列，归一化处理后形成对应的训练数据集（S1）；对电导率初始分布序列添加标准正态分布的噪声，利用深度学习网络，使用训练数据集训练变分自动编码器（S2）；根据边界测量电压数据集，使用训练好的变分自动编码器中的编码器生成网络中的隐藏层数据，以获取输入的电压数据特征（S3）；利用电压数据特征和训练好的变分自动编码器中的解码器，建立边界测量电压数据集和对应电导率分布序列之间的映射关系（S4）。解决了传统电阻抗成像中重建图像精度不高、抗噪性差、利用先验知识效率低等问题，提高了电阻抗成像的图像重建精度。

Description

一种基于深度学习的电阻抗成像方法 技术领域
本发明属于电阻抗成像技术领域，更具体的说是涉及一种基于深度学习的电阻抗成像方法。
背景技术
电阻抗成像技术是近几十年新出现的一种无创功能性检测方法，在医学成像、地质勘探和工业检测领域具有广阔的应用前景。电阻抗成像中的图像重建是一个典型的非线性的病态逆问题。传统的图像重建方法一是对成像目标泛函添加先验信息以进行正则化的技术，减轻EIT逆问题的病态性和欠定性。最普遍采用的是Tikhonov正则化，其对连续形式的电导率目标进行图像重建时，可以得到较好的重建效果。但是，在对具有跳跃式的电导率目标进行图像重建时，一般都会出现过度光滑现象，使得电导率重建图像的边缘变得模糊，器官与器官间边界存在伪影，在应用于医学成像时，具有明显的局限性。为了克服此问题，学者们提出总变分(TV)正则化，它是一种不通过施加平滑措施进行正则化的方法，它能够消弱EIT重建图像中的连续性，可保留重构结果中的非连续性变化，增强成像的间断性。但是，在处理逆问题过程中，如果目标函数具有连续性，采用总变差算法进行求解，则会出现阶梯效应，导致不能得到满意的重建效果。如人体肺部，既具有电导率变化明显的区域，也有电导率变化较缓和的区域。二是使用迭代类智能算法，包括卡尔曼滤波、经典神经网络等，它们简化了建模过程及问题的求解难度。但存在容易过拟合、对参数过于敏感以及对复杂的函数表示能力有限等缺陷。
因此，如何提供一种基于深度学习的电阻抗成像方法是本领域技术人员亟需解决的问题。
发明内容
有鉴于此，本发明提供了一种基于深度学习的电阻抗成像方法，解决了传统电阻抗成像中重建图像精度不高、抗噪性差、利用先验知识效率低等问题，提高了电阻抗成像的图像重建精度。
为了实现上述目的，本发明采用如下技术方案：
一种基于深度学习的电阻抗成像方法，包括如下步骤：
S1：获取待测区域的原始边界测量电压数据集，利用有限元法算出待测区域电导率的初始分布序列，归一化处理后形成对应的训练数据集；
S2：对步骤S1得到的电导率初始分布序列添加标准正态分布的噪声，利用深度学习网络，使用电导率分布的训练数据集训练变分自动编码器，以获取EIT图像的先验知识；
S3：利用深度学习网络，根据步骤S1中的边界测量电压数据集，使用步骤S2训练好的变分自动编码器中的编码器生成网络中的隐藏层数据，以获取输入的电压数据特征；
S4：根据步骤S1中的边界测量电压数据集，利用步骤S3训练好的电压数据特征和步骤S2中训练好的变分自动编码器中的解码器，建立边界测量电压数据集和对应电导率分布序列之间的映射关系。
优选的，步骤S1中的待测区域的原始边界测量电压数据集是通过16个电极阵列获取的。
优选的，深度学习网络的额外损失函数L为：
Figure PCTCN2021082933-appb-000001
其中，γ为电导率分布，Ψ为解码器函数，Φ为编码器函数，D kl为Kullback-Leibler发散损失项，m为迭代总次数，n为当前迭代次数，γ n为电导率，μ n为Φ的均值，σ n为Φ的标准差，N(0,1)为标准正态分布。
优选的，还包括如下步骤：
S5：通过步骤S1获得的未处理过的边界测量电压数据集和步骤S4所建立的训练好的映射关系，得到最终的电导率分布。
优选的，所述步骤S4与所述步骤S5之间还包括步骤S41：重复步骤S1～S4，对整个模型加以训练。
本发明的有益效果在于：
相对于传统的电阻抗成像方法中常用的正则化方法所产生的伪影问题、图像失真以及重建速度慢等问题以及浅层神经网络中容易过拟合、参数敏感、复杂函数表示能力有限等缺点，本发明提出了一种基于深度学习的电阻抗成像方法，通过步骤S1～S4采用逐层训练机制，首先训练好每层网络，然后优化训练好的网络，完成深度学习的训练。这一深层结构使深度学习具有极强的表达能力和学习能为，能够更好地用较少的参数逼近复杂函数，减少过拟合现象的产生。
通过步骤S2～S3，准确提取边界测量电压数据的特征，同时通过动态调整额外损失函数，充分利用先验知识，提高了图像重建精度。
通过步骤S2～S4的变分自编码器，可以对数据进行降维处理，训练速度快，成像效率高。
因此用基于变分自编码的深度学习能更准确地学习电导率分布的特征，能更好地表征边界测量电压与电导率分布之间的映射关系，进而建立EIT深度学 习网络模型，然后利用该模型获取未经训练的边界测量电压对应的电导率分布，完成更高速度、更高分辨率的图像重建。此外，通过不断积累高质量的训练数据，利用所发明方法不断进行训练，图像质量可不断提高。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。
图1附图为本发明的方法流程图。
图2附图为本发明深度学习网络的结构框架图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
请参阅附图1-2，本发明提供了一种基于深度学习的电阻抗成像方法，包括如下步骤：
S1：通过16个电极阵列获取待测区域的原始边界测量电压数据集，利用有限元法算出待测区域电导率的初始分布序列，归一化处理后形成对应的训练数据集；
S2：对步骤S1得到的电导率初始分布序列添加标准正态分布的噪声，利用深度 学习网络，使用电导率分布的训练数据集训练变分自动编码器，以获取EIT图像的先验知识；图像先验知识是将电导率初始分布和添加的标准正态分布的噪声的数据集，代入损失函数L，利用损失函数L算出自动编码器函数，即将初始分布中的特征融入到编码器函数中。
其中，深度学习网络的额外损失函数L为：
Figure PCTCN2021082933-appb-000002
其中，γ为电导率分布，Ψ为解码器函数：R k→R d,k＜d，Φ为编码器函数：R d→R k,k＜d，D kl为Kullback-Leibler发散损失项，m为迭代总次数，n为当前迭代次数，γ n为电导率，
Figure PCTCN2021082933-appb-000003
μ n为Φ的均值，σ n为Φ的标准差，N(0,1)为标准正态分布。
S3：利用深度学习网络，根据步骤S1中的边界测量电压数据集，使用步骤S2训练好的变分自动编码器中的编码器生成网络中的隐藏层数据，以获取输入的电压数据特征；
S4：根据步骤S1中的边界测量电压数据集，利用步骤S3训练好的电压数据特征和步骤S2中训练好的变分自动编码器中的解码器，建立边界测量电压数据集和对应电导率分布序列之间的映射关系；
S41：重复步骤S1～S4，对整个模型加以训练。整个模型是边界测量电压数据集和对应电导率分布序列之间的映射关系和中间的深度学习的计算步骤，这些关系和计算步骤形成了一个大的模型，每次有新的电压数据数据的时候就运行一次，得到最新的电导率，相当于训练了一次其中的计算网络。随着数据量的增加，训练量增加，那么计算步骤或者说深度学习网络效果越好。
S5：根据16个电极阵列获得的未处理过的边界测量电压数据集和步骤S4所建立的训练好的映射关系，得到最终的电导率分布。正常的生物体某种组织 的电导率是确定的，而且在一定范围内可以认为是均匀分布的。如果生物该组织中某一块病变，它的电导率会异常。本发明通过测量生物体内某种组织当前的电导率分布，并同正常健康体的参考值做对比，判断是否病变。
相对于传统的电阻抗成像方法中常用的正则化方法所产生的伪影问题、图像失真以及重建速度慢等问题以及浅层神经网络中容易过拟合、参数敏感、复杂函数表示能力有限等缺点，本发明提出了一种基于深度学习的电阻抗成像方法，通过步骤S1～S4采用逐层训练机制，首先训练好每层网络，然后优化训练好的网络，完成深度学习的训练。这一深层结构使深度学习具有极强的表达能力和学习能为，能够更好地用较少的参数逼近复杂函数，减少过拟合现象的产生。
通过步骤S2～S3，准确提取边界测量电压数据的特征，同时通过动态调整额外损失函数，充分利用先验知识，提高了图像重建精度。
通过步骤S2～S4的变分自编码器，可以对数据进行降维处理，训练速度快，成像效率高。
因此用基于变分自编码的深度学习能更准确地学习电导率分布的特征，能更好地表征边界测量电压与电导率分布之间的映射关系，进而建立EIT深度学习网络模型，然后利用该模型获取未经训练的边界测量电压对应的电导率分布，完成更高速度、更高分辨率的图像重建。此外，通过不断积累高质量的训练数据，利用所发明方法不断进行训练，图像质量可不断提高。
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的装置而言，由于其与实施例公开的方法相对应，所以描述的比较简单，相关之处参见方法部分说明即可。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。

Claims (5)

  1. 一种基于深度学习的电阻抗成像方法，其特征在于，包括如下步骤：
    S1：获取待测区域的原始边界测量电压数据集，利用有限元法算出待测区域电导率的初始分布序列，归一化处理后形成对应的训练数据集；
    S2：对步骤S1得到的电导率初始分布序列添加标准正态分布的噪声，利用深度学习网络，使用电导率分布的训练数据集训练变分自动编码器，以获取EIT图像的先验知识；
    S3：利用深度学习网络，根据步骤S1中的边界测量电压数据集，使用步骤S2训练好的变分自动编码器中的编码器生成网络中的隐藏层数据，以获取输入的电压数据特征；
    S4：根据步骤S1中的边界测量电压数据集，利用步骤S3训练好的电压数据特征和步骤S2中训练好的变分自动编码器中的解码器，建立边界测量电压数据集和对应电导率分布序列之间的映射关系。
  2. 根据权利要求1所述的一种基于深度学习的电阻抗成像方法，其特征在于，步骤S1中的待测区域的原始边界测量电压数据集是通过16个电极阵列获取的。
  3. 根据权利要求1所述的一种基于深度学习的电阻抗成像方法，其特征在于，深度学习网络的额外损失函数L为：
    Figure PCTCN2021082933-appb-100001
    其中，γ为电导率分布，Ψ为解码器函数，Φ为编码器函数，D kl为Kullback-Leibler发散损失项，m为迭代总次数，n为当前迭代次数，γ n为电导率，μ n为Φ的均值，σ n为Φ的标准差，N(0,1)为标准正态分布。
  4. 根据权利要求1所述的一种基于深度学习的电阻抗成像方法，其特征在于，还包括如下步骤：
    S5：通过步骤S1获得的未处理过的边界测量电压数据集和步骤S4所建立的训练好的映射关系，得到最终的电导率分布。
  5. 根据权利要求4所述的一种基于深度学习的电阻抗成像方法，其特征在于，所述步骤S4与所述步骤S5之间还包括步骤S41：重复步骤S1～S4，对整个模型加以训练。
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