Magnetic resonance image reconstruction method and device, equipment and medium

A magnetic resonance image and image reconstruction technology, which is applied in the field of medical imaging, can solve the problems of not being a data consistency guarantee method, the quality of reconstructed images needs to be improved, and it is not suitable for image reconstruction, so as to achieve the effect of improving image quality.

Active Publication Date: 2020-02-07
SHENZHEN INST OF ADVANCED TECH
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Problems solved by technology

[0005] However, the data fidelity item in the model applicable to the ADMM-net method is characterized by the 2-norm between the reconstructed k-space and the sampling point. This least square constraint is based on the premise of linear unbiased estimation. It is not the most effective data consistency guarantee method, and it is not suitable for all image reconstruction situations, resulting in the imaging quality of the reconstructed image to be improved

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  • Magnetic resonance image reconstruction method and device, equipment and medium
  • Magnetic resonance image reconstruction method and device, equipment and medium
  • Magnetic resonance image reconstruction method and device, equipment and medium

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Embodiment 1

[0047] figure 1 It is a flow chart of the magnetic resonance image reconstruction method provided by Embodiment 1 of the present invention, and this embodiment is applicable to medical image reconstruction.

[0048] Such as figure 1 As shown, the magnetic resonance image reconstruction method specifically includes the following steps:

[0049] S110. Acquire under-sampled magnetic resonance data.

[0050] Specifically, the under-sampled magnetic resonance data is the under-sampled magnetic resonance K-space data obtained by scanning the magnetic resonance imaging device according to a preset scanning manner.

[0051] K-space, also called Fourier space, is the filling space of the original data of magnetic resonance signals with spatial positioning coding information. Each magnetic resonance image has its corresponding K-space data lattice. The under-sampled K-space data is the data of not all sampling points, which can reduce the time of data sampling.

[0052]S120. Input t...

Embodiment 2

[0058] Figure 2a It is a flow chart of the image reconstruction model training method provided by Embodiment 2 of the invention. This embodiment further illustrates the process of image reconstruction model training on the basis of the foregoing embodiments.

[0059] Such as Figure 2a As shown, the process of image reconstruction model training specifically includes the following steps:

[0060] S210. Acquire fully-sampled magnetic resonance data, and extract at least one set of under-sampled data from the fully-sampled magnetic resonance data to obtain a data pair of at least one set of under-sampled data and fully-sampled magnetic resonance data.

[0061] This step is the model training sample collection process. For the full-sampled K-space data, the under-sampled data can be obtained by selecting the sampling data of the corresponding sampling line from the full-sampled data according to preset rules. . Exemplarily, in the full sampling process, there are 256 sampling...

Embodiment 3

[0079] Figure 4 It shows a schematic structural diagram of the magnetic resonance image reconstruction device in Embodiment 3 of the present invention. This embodiment is suitable for medical image reconstruction. The magnetic resonance image reconstruction device can be configured in medical equipment such as magnetic resonance imaging equipment and other computer equipment middle.

[0080] Such as Figure 4 As shown, the magnetic resonance image reconstruction apparatus specifically includes: a data acquisition module 410 and an image reconstruction module 420 .

[0081] Among them, the data acquisition module 410 is used to acquire under-sampled magnetic resonance data; the image reconstruction module 420 is used to input the magnetic resonance data into the image reconstruction model based on the alternating direction multiplier algorithm to obtain the reconstructed target magnetic resonance data. A resonance image, wherein the image reconstruction model is a pre-traine...

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Abstract

The embodiment of the invention discloses a magnetic resonance image reconstruction method and device, equipment and a medium. The method comprises the following steps: acquiring undersampled magneticresonance data; and inputting the magnetic resonance data into an image reconstruction model based on an alternating direction multiplier algorithm to obtain a reconstructed target magnetic resonanceimage, the image reconstruction model being a pre-trained result of a mathematical model in which a data fidelity term and a regular term are both uncertain terms. According to the embodiment of theinvention, the method solves a problem that a data fidelity term in a model suitable for an ADMM-net method is represented by employing a 2-norm between a reconstructed k space and a sampling point ina magnetic resonance image reconstruction process, is not a most effective data consistency guarantee method, and is not suitable for all image reconstruction conditions. The image quality after image reconstruction based on the ADMM algorithm can be improved.

Description

technical field [0001] Embodiments of the present invention relate to medical imaging technology, and in particular to a magnetic resonance image reconstruction method, device, equipment and 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 has no side effects on human body, so it has become a powerful tool for medical clinical diagnosis. [0003] In order to improve the speed and imaging quality of magnetic resonance imaging, deep learning methods are often used for image reconstruction, such as using neural networks to learn the optimal parameters required for reconstruction from a large number of training data or directly learning from under-acquisition data to full-acquisition images The mapping relationship between them can achieve better imaging quality and higher acceleration than traditional parallel imaging or compressed sen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00
CPCG06T11/005Y02A90/30
Inventor 梁栋程静王海峰朱燕杰刘新郑海荣
Owner SHENZHEN INST OF ADVANCED TECH
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