Electrical impedance tomography method based on deep learning

A technology of deep learning and tomography, applied in neural learning methods, 2D image generation, image generation, etc., can solve nonlinear problems

Inactive Publication Date: 2017-03-15
TIANJIN POLYTECHNIC UNIV
View PDF5 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method solves the nonlinear and ill-conditioned problems when solving the inverse problem of electrical impedance tomography, and improves the solution accuracy and image reconstruction quality of the inverse problem

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Electrical impedance tomography method based on deep learning
  • Electrical impedance tomography method based on deep learning
  • Electrical impedance tomography method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0049] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings and examples. The specific examples described here are only used to explain the present invention, not to limit the present invention.

[0050] figure 1 Shown is the schematic diagram of the EIT system. By applying electrode arrays and current excitation around the measured object field, the boundary measurement voltage sequence is obtained, and then the sequence and a certain reconstruction algorithm are used to reconstruct the internal distribution of the measured object field.

[0051] This example adopts the method of connecting 16 electrodes to obtain the boundary measurement voltage sequence (at this time, each group of boundary measurement voltage sequence has 208 voltage values, and the measured field is divided into 812 parts, corresponding to 812 conductivity va...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The present invention relates to an electrical impedance tomography (EIT) method based on deep learning, and is applied to the technical fields of medical imaging, industrial process image and geological prospecting, etc. The method comprises: obtaining an original boundary measurement voltage sequence and a conductivity distribution sequence, and performing normalization processing to obtain a training sample set; establishing an initial EIT deep learning network model, and training the EIT deep learning network model according to the training sample set and the setting training mode to allow the EIT deep learning network model obtained through training to represent the mapping relation between the original boundary measurement voltage sequence and the conductivity distribution sequence; and inputting the boundary measurement voltage sequence to the mapping relation to obtain the conductivity distribution sequence, and finally recovering the conductivity distribution sequence to a matrix mode to obtain an EIT image. The electrical impedance tomography method based on deep learning simplifies the modeling process and the solution difficulty of problems so as to solve the nonlinearity and ill-conditioned problems when the electrical impedance inverse problem is solved and improve the solution precision of the inverse problem and the image reconstruction quality.

Description

technical field [0001] The invention belongs to the technical field of electrical impedance tomography (Electrical Impedance Tomography, EIT), and in particular relates to an electrical impedance tomography method based on deep learning. Background technique [0002] Electrical tomography (Electrical Tomography, ET) is a new process tomography technology based on the sensitive mechanism of electrical properties that emerged in the late 1980s. Its physical basis is that different media have different electrical properties ( Conductivity / dielectric coefficient / complex admittance / permeability), by judging the distribution of electrical properties of objects in the sensitive field, the distribution of the medium in the field can be inferred. Electrical tomography technology has broad application prospects in multiphase flow and biomedical fields, and can realize long-term and continuous monitoring. [0003] Electrical impedance tomography applies a certain alternating current o...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08G06T11/00
CPCG06N3/084G06T11/005G06T2211/416
Inventor 汪剑鸣代月霞王琦李秀艳段晓杰孙玉宽
Owner TIANJIN POLYTECHNIC UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap