Electrical impedance tomography image reconstruction method based on residual network

A technology of electrical impedance tomography and image reconstruction, which is applied in the field of biomedical imaging and deep learning, can solve the problems of poor imaging quality and achieve the effects of improved precision, clear image presentation and accurate prediction

Pending Publication Date: 2020-11-27
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the defects of poor imaging quality of the existing imaging technology, the present invention provides a method for reconstructing electrical impedance tomography images based on residual networks, which combines image processing methods to reconstruct images

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  • Electrical impedance tomography image reconstruction method based on residual network
  • Electrical impedance tomography image reconstruction method based on residual network
  • Electrical impedance tomography image reconstruction method based on residual network

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Embodiment Construction

[0032] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0033] With reference to accompanying drawing, a kind of electrical impedance tomography image reconstruction method based on deep learning comprises the following steps:

[0034] S1: Use MIT equipment to make human skull models and heterogeneous objects, and design different frequencies for data collection;

[0035] S2: Transform the collected data from a one-dimensional vector form into a multi-channel matrix form similar to pictures;

[0036] S3: According to the particularity of the imaging problem, modify the residual network structure, and customize the training loss function for training;

[0037] S4: Use the training results to generate data for image processing and optimization.

[0038] In the step S1, the corresponding figure 1 Make the human skull model and heterogeneous objects in the process, and put th...

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Abstract

An electrical impedance tomography image reconstruction method based on a residual network comprises the following steps: 1) manufacturing a human skull model and a heterogeneous object by adopting MIT equipment, and designing different frequencies for data acquisition; 2) converting the acquired data from a one-dimensional vector form into a multi-channel matrix form similar to a picture; 3) aiming at imaging problem particularity, modifying a residual error network structure, and customizing a training loss function for training; and 4) generating data by adopting a training result to process image processing and optimization. According to the method, training is carried out by combining the deep learning technology with the actual MIT equipment acquired data, and compared with a traditional imaging method, the contour of an imaged object can be finer, and the position of the imaged object can be more accurate.

Description

technical field [0001] The invention relates to the fields of biomedical imaging and deep learning, in particular to a method for reconstructing an electrical impedance tomography image. Background technique [0002] Electrical Impedance Tomography (EIT, Electrical Impedance Tomography) is a new type of medical imaging technology improved by referring to geological detection equipment. By applying a weak excitation current to the measured field, and then detecting the surrounding area, the internal resistance of the human body is reconstructed. Impedance value or change value of electrical impedance. In addition, the electrical properties (conductivity, dielectric constant) of biological tissue can accurately reflect the physiological structure and pathological state of biological tissue. Studies have shown that there is a large difference in the electrical impedance characteristics between diseased tissue and normal tissue. . Therefore, as an important indicator for evalu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00G06N3/04G06N3/08G06F17/16
CPCG06T11/003G06N3/082G06F17/16G06N3/045
Inventor 宣琦孙翊杰宋栩杰袁琴翔云邱君瀚
Owner ZHEJIANG UNIV OF TECH
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