A MRI multi-channel reconstruction method based on deep learning and self-consistent data

A technology of deep learning and magnetic resonance, applied in magnetic resonance measurement, measurement using nuclear magnetic resonance image system, measurement of magnetic variables, etc., can solve the problems of low acceleration factor, low processing capacity of high-magnification undersampling data, etc., to improve quality , improve the generalization ability, and the effect of accurate end-to-end mapping relationship

Active Publication Date: 2020-12-04
朱高杰
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Problems solved by technology

[0006] The object of the present invention is: the present invention provides a magnetic resonance multi-channel reconstruction method based on deep learning and data self-consistency, which solves the problem that the existing magnetic resonance reconstruction method based on data self-consistency is limited by various factors. Low and high undersampling data processing ability is low and the MRI reconstruction method based on deep learning can only deal with single-channel data

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  • A MRI multi-channel reconstruction method based on deep learning and self-consistent data
  • A MRI multi-channel reconstruction method based on deep learning and self-consistent data
  • A MRI multi-channel reconstruction method based on deep learning and self-consistent data

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Embodiment 1

[0044] A magnetic resonance multi-channel reconstruction method based on deep learning and data self-consistency, comprising the following steps:

[0045] Step 1: Collect multi-channel full-sampling training set K-space data, convert the under-sampling test set K-space data into convoluted images as the input of the reconstruction network, and use the full-sampled K-space data as the training label data for the reconstruction network And generate convolution kernels based on fully sampled K-space data;

[0046] Step 2: After inputting the convoluted image and fusing the under-sampled test set K-space data and convolution kernel, the reconstruction network constructed by repeatedly superimposing SC layer, CNN network and DC layer takes the training label data as the target and backpropagation Train the network parameters to obtain the mapping relationship between the input and output of the reconstructed network;

[0047] Step 3: Input the undersampled test set K-space data in...

Embodiment 2

[0050] Step 1 includes the following steps:

[0051] Step 1.1: Use multi-channel receiving coils to collect full-sampled training set K-space data, and generate under-sampled test set K-space data S through artificial undersampling u , whose size is N x *N y *N c , where N x Represents the collected data S u the number of rows, N y represents the data S u the number of columns, N c Represents the number of receiving channels; manual undersampling does not require manual operation, and can be automated with a variety of algorithms; in the training phase, the undersampling data is generated by discarding some of the full sampling data; in the testing phase, the MRI scanner Undersampling data can be obtained directly; a full sampling data can only have one corresponding undersampling data, so in actual operation, after obtaining a full sampling data, artificial undersampling generates undersampling data on this basis.

[0052] Step 1.2: Undersampling test set K-space data...

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Abstract

The invention discloses a magnetic resonance multi-channel reconstruction method based on deep learning and data self-consistence and belongs to the magnetic resonance reconstruction method field. Themethod includes the following steps that: multi-channel full-sampling training set data are collected, under-sampled data are converted into wrap-around images which are used as the input of a reconstruction network, and full-sampled data are used as training mark data, and a convolution kernel is generated based the full-sampled data; 2, the data in the step 1 are inputted, on the basis of the reconstruction network constructed by means of the repeated superposition of SC layers, CNNs, and DC layers, and with the training mark data adopted as an objective, network parameters are trained withback propagation, so that mapping relationships between the input and output of the reconstruction network are obtained; and 3, test set data are inputted into the reconstruction network so as to besubjected to forward propagation, so that unknown mapping data are obtained, and the reconstruction of magnetic resonance is completed. With the method adopted, a problem that an existing resonance reconstruction method can only process single-channel data can be solved; more stable and accurate end-to-end mapping relationships can be obtained; the quality of magnetic resonance reconstruction canbe fundamentally improved; and magnetic resonance scanning time can be significantly shortened.

Description

technical field [0001] The invention relates to the field of magnetic resonance reconstruction methods, in particular to a magnetic resonance multi-channel reconstruction method based on deep learning and data self-consistency. Background technique [0002] Magnetic resonance imaging technology is a technology that uses the nuclear magnetic resonance phenomenon of hydrogen protons for imaging; atomic nuclei containing singular protons in the human body, such as the ubiquitous hydrogen atomic nucleus, whose protons have spin motion; the spin motion of charged atomic nuclei, in physics. It is similar to a single small magnet, and the directional distribution of the small magnet is random without the influence of external conditions; when the human body is placed in an external magnetic field, the small magnet will be rearranged according to the magnetic field lines of the external magnetic field. The two directions of antiparallel to the magnetic field lines of the external ma...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R33/56
CPCG01R33/56
Inventor 朱高杰
Owner 朱高杰
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