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

Parallel magnetic resonance imaging GRAPPA (generalized autocalibrating partially parallel acquisitions) method based on machine learning

A technology of magnetic resonance imaging and machine learning, which is applied in the fields of instruments, measuring magnetic variables, measuring devices, etc., and can solve the problem of large deviation of reconstruction results.

Active Publication Date: 2012-11-28
SHANGHAI UNITED IMAGING HEALTHCARE
View PDF4 Cites 31 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The weight coefficient is estimated by using the self-calibration line and neighboring points, but because the actual measured signal value is not the real value of the signal, but contains noise
Therefore, the final reconstruction result will have a large deviation

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
  • Parallel magnetic resonance imaging GRAPPA (generalized autocalibrating partially parallel acquisitions) method based on machine learning
  • Parallel magnetic resonance imaging GRAPPA (generalized autocalibrating partially parallel acquisitions) method based on machine learning
  • Parallel magnetic resonance imaging GRAPPA (generalized autocalibrating partially parallel acquisitions) method based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] Please refer to figure 1 The present invention discloses a GRAPPA method of parallel magnetic resonance imaging based on machine learning, which includes the following steps:

[0019] Collect K-space data sets from the object to be imaged;

[0020] Use regression analysis in machine learning to establish the mapping relationship between under-sampled points and their neighbors;

[0021] Predict the under-sampling points and fill the under-sampling K space;

[0022] According to the K-space data of each coil, the inverse Fourier transform is performed to obtain the image of each coil, and the square sum of multiple images is obtained to obtain the final reconstruction result.

[0023] For the first step above, the K-space data set includes self-calibration lines. The sampling method is the same as the traditional GRAPPA sampling method. The sampling mode is determined by the sampling rate and the number of calibration lines. It is assumed that there are 256 lines in the K-space ph...

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 discloses a parallel magnetic resonance imaging GRAPPA (generalized autocalibrating partially parallel acquisitions) method based on machine learning. The method comprises the steps of: acquiring K spatial data set from a to-be-imaged object, creating mapping relation between an undersampled point and a neighbor point by virtue of regression analysis in the machine learning, predicting the undersampled point, and filling up the undersampled K space, performing Fourier inverse transformation to K spatial data of each coil to obtain the image of each coil, and solving quadratic sum of multiple images to obtain the last reconstructed result. Based on the method, the mapping relation between the undersampled point and the neighbor point is estimated by virtue of the regression analysis in the machine learning, and the linear mapping relation in the original algorithm is replaced, and the undersampled space is filled up, at last the more accurate reconstructed result can be obtained, so that the artifact of the magnetic resonance reconstructed image can be reduced.

Description

Technical field [0001] The invention relates to a magnetic resonance imaging technology, in particular to a method for parallel acquisition and image reconstruction. Background technique [0002] In order to increase the speed of magnetic resonance image acquisition, parallel imaging technology is widely used in magnetic resonance imaging. This technology mainly uses the spatial sensitivity difference of a single receiving coil in the phased array coil to encode spatial information, reduces the number of phase encoding steps necessary for imaging, and obtains a faster scanning speed. Parallel imaging technology is mainly divided into two categories: k-space method and image domain method. GRAPPA (Generalized autocalibrating partially parallel acquisitions) reconstruction technology is an image reconstruction technology based on k-space. [0003] The traditional GRAPPA method assumes that there is a certain linear relationship between K-space data points, that is, any data point ca...

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): G01R33/561
Inventor 梁栋朱燕杰吴垠刘新郑海荣
Owner SHANGHAI UNITED IMAGING HEALTHCARE
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