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

Multi-contrast MRI image reconstruction method based on deep learning

A deep learning and image reconstruction technology, applied in image data processing, 2D image generation, instruments, etc., can solve problems such as the inability to guarantee the effect of MRI image reconstruction, and achieve the effect of improving reconstruction quality and ensuring accuracy and reliability.

Active Publication Date: 2021-04-23
GUANGDONG UNIV OF TECH
View PDF9 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that the current MRI image reconstruction method cannot guarantee the MRI image reconstruction effect under multi-contrast ratios, the present invention proposes a multi-contrast MRI image reconstruction method based on deep learning to improve the reconstruction quality of MRI images and ensure the accuracy and accuracy of the diagnostic results of the medical system. reliability

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
  • Multi-contrast MRI image reconstruction method based on deep learning
  • Multi-contrast MRI image reconstruction method based on deep learning
  • Multi-contrast MRI image reconstruction method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] The positional relationship described in the drawings is only for illustrative purposes and cannot be construed as a limitation to this patent;

[0059] Such as figure 1 The flow chart of the multi-contrast MRI image reconstruction method based on deep learning shown in figure 1 , the steps of the method include:

[0060] A multi-contrast MRI image reconstruction method based on deep learning, at least comprising:

[0061] S1. Collect real full-sampled MRI images to form the real labels of the training set as supervision, and use the training set samples to train a deep convolutional neural network Convnet(·);

[0062] S2. Put T 1 The contrast undersampled MRI image is used as the input of the deep convolutional neural network Convnet( ), and the output T 1 Contrast initially fully sampled MRI images; the T 2 The contrast undersampled MRI image is used as the input of the deep convolutional neural network Convnet( ), and the output T 2 Contrast initially fully sam...

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 provides a multi-contrast MRI image reconstruction method based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: collecting a real full-sampling MRI image and a reconstructed MRI image randomly sampled in a K space to form a training set sample, and training a deep convolutional neural network; then taking the under-sampled MRI images with different contrast ratios as input, outputting preliminary full-sampled MRI images with different contrast ratios, extracting structural features and contrast ratio features of the preliminary full-sampled MRI images with different contrast ratios by an encoder, then performing similarity constraint, and finally generating final complete MRI images with different contrast ratios by a generator. According to the method, the defects that at present, most models are reconstructed only through a single-contrast MRI image, and reconstruction lacks of utilization of multi-contrast MRI image associated information are overcome, the reconstruction quality of the MRI image is improved, and the reliability of a diagnosis result of a medical system is guaranteed.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, and more specifically, to a multi-contrast MRI image reconstruction method based on deep learning. Background technique [0002] Magnetic resonance imaging (magnetic resonance imaging, referred to as MRI) is an important and widely used medical imaging technology, which can be used for imaging the internal structure of the human body. Usually, 1R scanning can obtain images with different contrasts, such as T1 and T2 contrasts. In actual diagnosis, doctors need to combine complete MRI images with multiple contrasts for disease diagnosis. Therefore, multiple long-term scans are required for the patient, but the scanning time is increased. Not only will it cause discomfort to the patient, but it may also introduce motion artifacts and reduce the efficiency of MRI use; and reducing the scan time will reduce the amount of data collected, which in turn will lead to a decline in...

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): G06T11/00G06N3/04
Inventor 蔡越罗玉凌捷柳毅
Owner GUANGDONG UNIV OF 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