Magnetic resonance imaging method, device and system and storage medium

A magnetic resonance imaging and magnetic resonance image technology, applied in the field of deep learning, can solve the problems of limited parallel imaging acceleration multiple, image noise amplification, difficult selection of sparse transformation and reconstruction parameters, etc., to achieve the effect of improving the degree of freedom and quality

Pending Publication Date: 2020-10-30
SHENZHEN INST OF ADVANCED TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, limited by hardware and other conditions, the acceleration of parallel imaging is limited, and with the increase of the acceleration, the image will appear noise amplification phenomen

Method used

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  • Magnetic resonance imaging method, device and system and storage medium
  • Magnetic resonance imaging method, device and system and storage medium
  • Magnetic resonance imaging method, device and system and storage medium

Examples

Experimental program
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Example Embodiment

[0029] Example one

[0030] figure 2 This is a schematic flow chart of a magnetic resonance imaging method provided in the first embodiment of the present invention. This embodiment is applicable to the case of performing magnetic resonance imaging based on a neural network. The method can be executed by the magnetic resonance imaging apparatus provided in the embodiments of the present application. Specifically include the following steps:

[0031] S110. Obtain an original model of magnetic resonance imaging, and establish an initial imaging model according to an iterative algorithm used to solve the original model. The iterative algorithm includes at least one of undetermined parameters, undetermined solving operators, and undetermined structural relationships.

[0032] S120. Training the initial imaging model based on the sample data to generate a magnetic resonance imaging model, where the training of the initial imaging model is used to learn the undetermined parameters, undete...

Example Embodiment

[0066] Example two

[0067] Picture 9 It is a schematic structural diagram of a magnetic resonance imaging apparatus provided in the second embodiment of the present invention, and the magnetic resonance imaging apparatus includes:

[0068] The initial imaging model establishment module 210 is used to obtain the original model of magnetic resonance imaging, and establish the initial imaging model according to the iterative algorithm used to solve the original model. The iterative algorithm includes undetermined parameters, undetermined solving operators, and undetermined structural relationships At least one of

[0069] The model training module 220 is configured to train the initial imaging model based on sample data to generate a magnetic resonance imaging model, wherein the training of the initial imaging model is used to learn the undetermined parameters and undetermined solving operators in the iterative algorithm At least one of the relationship with the pending structure;

[...

Example Embodiment

[0090] Example three

[0091] Picture 10 Is a schematic structural diagram of a magnetic resonance system provided in the third embodiment of the present invention, Picture 10 Showing a block diagram of an exemplary medical imaging system suitable for implementing embodiments of the present invention, Picture 10 The medical imaging system shown is only an example, and should not bring any limitation to the function and application scope of the embodiment of the present invention.

[0092] The magnetic resonance system includes a magnetic resonance device 300 and a computer 400.

[0093] The computer 400 may be used to implement specific methods and apparatuses disclosed in some embodiments of the present invention. The specific device in this embodiment uses a functional block diagram to show a hardware platform including a display module. In some embodiments, the computer 400 may implement specific implementations of some embodiments of the present invention through its hardware...

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Abstract

The invention discloses a magnetic resonance imaging method, device and system and a storage medium. The magnetic resonance imaging method comprises the steps of: acquiring an original model of magnetic resonance imaging, and establishing an initial imaging model according to an iterative algorithm used for solving the original model, wherein the iterative algorithm comprises at least one of an undetermined parameter, an undetermined solving operator and an undetermined structure relation; training the initial imaging model based on the sample data to generate a magnetic resonance imaging model, wherein the training of the initial imaging model is used for learning at least one of the undetermined parameter, the undetermined solving operator and the undetermined structural relationship inthe iterative algorithm; and acquiring to-be-processed undersampled K spatial data, and inputting the undersampled K spatial data into the magnetic resonance imaging model to generate a magnetic resonance image. Compared with a traditional mode, the magnetic resonance imaging method improves the degrees of freedom of the magnetic resonance imaging model, and the magnetic resonance imaging model obtained through learning can improve the quality of a magnetic resonance reconstructed image.

Description

technical field [0001] Embodiments of the present invention relate to deep learning technology, and in particular to a magnetic resonance imaging method, device, system, and storage medium. Background technique [0002] Magnetic resonance uses static magnetic field and radio frequency magnetic field to image human tissue. It not only provides rich tissue contrast, but also is harmless to human body, so it has become a powerful tool for medical clinical diagnosis. However, the slow imaging speed has always been a major bottleneck restricting its rapid development. [0003] In terms of fast imaging, the commonly used technologies are parallel imaging and compressed sensing. Parallel imaging uses the correlation between multi-channel coils to accelerate acquisition, while compressed sensing uses the prior information of the sparseness of the imaged object to reduce k-space sampling points. However, limited by hardware and other conditions, the acceleration of parallel imaging...

Claims

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Application Information

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IPC IPC(8): G01R33/48G01R33/56
CPCG01R33/4818G01R33/5608G01R33/561G01R33/56545A61B5/055
Inventor 梁栋程静王海峰郑海荣刘新
Owner SHENZHEN INST OF ADVANCED TECH
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