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, long reconstruction time of compressed sensing technology, etc., to achieve the effect of improving accuracy and degree of freedom

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

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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

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Experimental program
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Embodiment 1

[0030] figure 2 It is a flow chart of a magnetic resonance imaging method provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of magnetic resonance imaging based on a neural network, and the method can be executed by the magnetic resonance imaging device provided in the embodiment of the present application. Specifically include the following steps:

[0031] S110. Establish an initial network model according to the original model of the magnetic resonance imaging and an iterative algorithm for solving the original model, wherein the iterative algorithm includes an undetermined solution operator and an undetermined parameter structure relationship.

[0032] S120. Input the undersampled K-space data of the sample into the initial network model to obtain an output magnetic resonance image of the network model, and generate a standard magnetic resonance image based on the output magnetic resonance image and the full-sampled K-space data of...

Embodiment 2

[0058] Figure 7 It is a schematic structural diagram of a magnetic resonance imaging device provided in Embodiment 2 of the present invention, and the magnetic resonance imaging device includes:

[0059] An initial network model establishment module 210, configured to establish an initial network model according to the original model of magnetic resonance imaging and an iterative algorithm for solving the original model, wherein the iterative algorithm includes undetermined solution operators and undetermined parameter structure relationships;

[0060] The loss function determination module 220 is configured to input the undersampled K-space data of the sample into the initial network model to obtain the output magnetic resonance image of the network model, and according to the output magnetic resonance image and the full-sampled K-space of the sample The standard MRI images generated from the data determine the loss function;

[0061] A network model training module 230, co...

Embodiment 3

[0080] Figure 8 It is a schematic structural diagram of a magnetic resonance system provided in Embodiment 3 of the present invention, Figure 8 A block diagram of an exemplary medical imaging system suitable for implementing embodiments of the invention is shown, Figure 8 The medical imaging system shown is only an example, and should not impose any limitation on the functions and scope of use of the embodiments of the present invention.

[0081] The magnetic resonance system includes a magnetic resonance device 500 and a computer 600 .

[0082] The computer 600 can be used to implement specific methods and devices 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 600 can implement some embodiments of the present invention through its hardware devices, software programs, firmware and their combinations. In...

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Abstract

An embodiment of the invention discloses a magnetic resonance imaging method, device and system and a storage medium. The magnetic resonance imaging method comprises the steps of: establishing an initial network model according to an original model of magnetic resonance imaging and an iterative algorithm used for solving the original model, wherein the iterative algorithm comprises an undeterminedsolving operator and an undetermined parameter structure relation; inputting undersampled K space data of a sample into the initial network model to obtain an output magnetic resonance image of the network model, and determining a loss function according to the output magnetic resonance image and a standard magnetic resonance image of the sample; and adjusting network parameters in the initial network model according to the loss function, and generating a network model for magnetic resonance imaging, wherein the network parameters in the initial network model are used for replacing the undetermined solving operator and the undetermined parameter structure relation in the iterative algorithm. According to the magnetic resonance imaging method, the to-be-processed undersampled K space datais acquired, and the undersampled K space data is input to the network model for magnetic resonance imaging to generate the magnetic resonance image, so that the quality of the magnetic resonance image is improved.

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): G01R33/56G01R33/48
CPCG01R33/5608G01R33/4818
Inventor 梁栋程静王海峰郑海荣刘新
Owner SHENZHEN INST OF ADVANCED TECH
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