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
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The weight coefficient is estimated by using the self-calibration line and neighboring points, but because the actual measured signa

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  • 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

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[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...

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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...

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Application Information

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IPC IPC(8): G01R33/561
Inventor 梁栋朱燕杰吴垠刘新郑海荣
Owner SHANGHAI UNITED IMAGING HEALTHCARE
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