Magnetic resonance reconstruction method based on deep learning and data consistency

A deep learning and consistent technology, applied in magnetic resonance measurement, measurement using nuclear magnetic resonance imaging system, measuring magnetic variables, etc., can solve problems such as poor stability, poor reconstruction performance, and unable to improve the collected data, so as to avoid stability poor performance, improved rebuild performance and stability, effects that favor consistency and stability

Active Publication Date: 2018-11-16
朱高杰
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Problems solved by technology

[0005] The purpose of the present invention is: the present invention provides a magnetic resonance reconstruction method based on deep learning and data consistency, which solves the problem that the existing deep learning-based magne

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  • Magnetic resonance reconstruction method based on deep learning and data consistency
  • Magnetic resonance reconstruction method based on deep learning and data consistency
  • Magnetic resonance reconstruction method based on deep learning and data consistency

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Experimental program
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Embodiment 1

[0060] Step 1 includes the following steps:

[0061] Step 1.1: Use multi-channel receiving coils to collect multi-channel K-space data to complete receiving data;

[0062] Step 1.2: A preliminary network composed of convolutional neural network and data consistency layer in sequence;

[0063] Step 1.3: Integrate the collected multi-channel K-space data into the preliminary network to complete the network construction.

[0064] Step 2 includes the following steps:

[0065] Step 2.1: Obtain undersampled data through artificially undersampled K-space data, that is, undersampled multi-channel K-space data, and the size of the undersampled data is: N x *N y *N c , where N x Represents the number of rows of collected data, N y Represents the number of columns of data, N c Represents the number of receiving channels, and the remaining area of ​​K-space data is full sampling data;

[0066] Step 2.2: Subsampling the multi-channel K-space data S u Obtain its corresponding multi...

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Abstract

The invention discloses a magnetic resonance reconstruction method based on deep learning and data consistency, and relates to the field of magnetic resonance reconstruction methods. The method comprises the steps: 1, collecting K-space data and integrating the K-space data into a network, formed by the sequential stacking of a convolutional neural network and a data consistency layer, for completing the network construction; 2, converting the undersampled data in the K-space data into a roll pleat image as the input of the built network, converting the full-sampled data of the K-space data into a complete image as the tag data of the built network, and obtaining a mapping relation between the network input and output through a back propagation training network; 3, inputting the corresponding images of a test set into the trained network, and performing the forward propagation to obtain an output image to complete the magnetic resonance reconstruction. The method solves the problems ofpoor reconstruction performance and stability caused by a condition that a conventional magnetic resonance reconstruction method based on deep learning does not fully utilize the collected data and can only deal with a single channel, achieves implementation supervision, improves the learning ability, and improves reconstruction performances.

Description

technical field [0001] The invention relates to the field of magnetic resonance reconstruction methods, in particular to a magnetic resonance reconstruction method based on deep learning and data consistency. Background technique [0002] Magnetic resonance imaging is a technique that uses the nuclear magnetic resonance phenomenon of hydrogen protons for imaging. Nuclei containing a single number of protons in the human body, such as the ubiquitous hydrogen nucleus, have spin motions for the protons. The spin motion of charged atomic nuclei is physically similar to individual small magnets, and the directionality distribution of these small magnets is random in the absence of external conditions. When the human body is placed in an external magnetic field, these small magnets will rearrange according to the magnetic force lines of the external magnetic field, specifically in two directions parallel to or antiparallel to the external magnetic field magnetic force lines, and ...

Claims

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Application Information

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IPC IPC(8): G01R33/54G01R33/561
CPCG01R33/54G01R33/5619
Inventor 朱高杰
Owner 朱高杰
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