Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Substance decomposition method based on plain-scan CT (Computed Tomography), intelligent terminal and storage medium

A substance separation and substance technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problems of high sensitivity to parameter selection and affect the accuracy of final substance decomposition results, and achieve the effect of improving accuracy.

Pending Publication Date: 2022-04-01
SHENZHEN INST OF ADVANCED TECH
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the related technologies mentioned above, the inventor believes that the result of the direct decomposition imaging method is highly sensitive to the selection of parameters, that is, the final material decomposition result is more easily affected by various parameters, thereby affecting the accuracy of the final material decomposition result

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
  • Substance decomposition method based on plain-scan CT (Computed Tomography), intelligent terminal and storage medium
  • Substance decomposition method based on plain-scan CT (Computed Tomography), intelligent terminal and storage medium
  • Substance decomposition method based on plain-scan CT (Computed Tomography), intelligent terminal and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0061] The embodiment of the present application discloses a material decomposition method based on plain scan CT, an intelligent terminal and a storage medium.

[0062] refer to figure 1 , a material decomposition method based on unenhanced CT, comprising:

[0063] S100. Acquire an original image.

[0064] In a specific implementation, the original image can be artificially input to the smart terminal for processing by the processing module, or the processing module can directly obtain the original image of ordinary plain scan CT.

[0065] S200. Construct an overall framework of the Generative Adversarial Network based on the preset Transformer generator module, the preset discriminator structure module and the preset loss function.

[0066] The overall framework of the generative confrontation network consists of a Transformer generator module based on a codec structure, a discriminator structure module, and a preset loss function. The Transformer generator module uses two...

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 invention relates to a substance decomposition method based on plain-scan CT, an intelligent terminal and a storage medium, and belongs to the technical field of medical image diagnosis. Constructing an overall framework of the generative adversarial network based on a preset Transform generator module, a preset discriminator structure module and a preset loss function; and inputting the original image into the overall frame, and obtaining a substance separation image. The method has the advantages that the image after substance separation is learned based on the original image of the traditional plain-scan CT, the substance separation effect from the traditional plain-scan CT to the dual-energy CT is achieved, and the substance decomposition precision is improved.

Description

technical field [0001] The present invention relates to the technical field of medical image diagnosis, in particular to a material decomposition method based on plain CT, an intelligent terminal and a storage medium. Background technique [0002] Computed tomography (CT), as an important medical imaging diagnostic technology in my country, has become one of the most common medical examination methods in major hospitals. At present, CT technology is mainly divided into traditional plain scan CT and dual-energy CT. The difference lies in the dual-energy CT can carry out precise quantitative analysis of substances, while traditional plain scan CT does not have the function of substance quantification, and secondly, dual-energy CT is more expensive than traditional plain scan CT. At present, in order to reduce the cost, most medical units are researching the method of providing additional quantitative information of material separation on the basis of traditional CT, that is, st...

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
IPC IPC(8): G06V10/77G06V10/774G06V10/82G06K9/62G06N3/08G06N3/04
CPCG06V10/82G06N3/04G06V10/77G06N3/08G06V10/774
Inventor 王国帅刘周胡战利罗德红罗虹虹梁栋郑海荣
Owner SHENZHEN INST OF ADVANCED TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com