Magnetic resonance image processing method, device, storage medium and magnetic resonance imaging system

A magnetic resonance image and processing method technology, applied in the field of medical image processing, can solve the problems of difficult control of constraints, unnatural details of magnetic resonance images, poor mosaic effect suppression effect, etc., and achieves improved suppression degree and artifact correction. good effect

Active Publication Date: 2022-08-02
SHANGHAI UNITED IMAGING INTELLIGENT MEDICAL TECH CO LTD
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Problems solved by technology

However, the interpolation method of magnetic resonance image domain data is not effective in suppressing the mosaic effect due to insufficient resolution; and the constraint strength of the k-space data extrapolation method is not easy to control. If the constraint is too loose, it cannot be effective. Suppresses heavy truncation artifacts, if the constraints are too heavy, it will modify the appearance of the MRI image, making the details of the MRI image look unnatural

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  • Magnetic resonance image processing method, device, storage medium and magnetic resonance imaging system
  • Magnetic resonance image processing method, device, storage medium and magnetic resonance imaging system
  • Magnetic resonance image processing method, device, storage medium and magnetic resonance imaging system

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Embodiment 1

[0030] During the magnetic resonance imaging process, due to the lack of high-frequency components in the k-space, serious Gibbs artifacts will occur in the final image. The magnetic resonance image processing method provided in this embodiment can be applied to the artificial Shadow correction, especially suitable for reducing Gibbs artifacts or truncation artifacts in magnetic resonance images. The method may be performed by a magnetic resonance image processing apparatus, which may be implemented in software and / or hardware, and the apparatus may be integrated in a device with image processing functions, such as a notebook computer, a desktop computer, or a server. see figure 1 , the method of this embodiment specifically includes the following steps:

[0031] S110: Acquire data to be corrected, and input the data to be corrected into an artifact correction model to generate initial correction data.

[0032] The collection of the data to be corrected may be unidirectional...

Embodiment 2

[0049] In this embodiment, on the basis of the above-mentioned embodiment, "generate the first weight matrix and the second weight matrix according to the preset weight value distribution rule" is added. On this basis, it is possible to further optimize the "weighting processing of the data to be corrected and the initial correction data to generate a weighted result". The explanations of terms that are the same as or corresponding to the above embodiments are not repeated here. see Figure 3A , the magnetic resonance image processing method provided by this embodiment includes:

[0050] S210: Acquire data to be corrected, and input the data to be corrected into an artifact correction model to generate initial correction data.

[0051] like Figure 3B is a schematic diagram of an exemplary artifact correction model described according to some embodiments of the present application. The artifact correction model may employ a neural network model, which may include an input ...

Embodiment 3

[0074] This embodiment provides a magnetic resonance image processing device, see Figure 4 , the device specifically includes:

[0075] The initial correction data generation module 410 is used to obtain the data to be corrected, and input the data to be corrected into the artifact correction model to generate initial correction data, wherein the artifact correction model is obtained by pre-training based on the neural network model, and the k corresponding to the initial correction data is obtained. The space includes more high-frequency components than the k-space corresponding to the data to be corrected;

[0076] The weighted fusion module 420 is used to perform weighting processing on the data to be corrected and the initial correction data, respectively, to generate a weighted result, and perform fusion processing on the two weighted results to generate target corrected k-space data;

[0077] The reconstruction module 430 is used for reconstructing the target-corrected...

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Abstract

The embodiments of the present invention disclose a magnetic resonance image processing method, a device, a storage medium and a magnetic resonance imaging system. The method includes: acquiring data to be corrected, and inputting the data to be corrected into an artifact correction model to generate initial correction data, wherein the artifact correction model is obtained by pre-training based on a neural network model, and the k-space corresponding to the initial correction data includes a There are many high-frequency components in the k-space corresponding to the data; weighting processing is performed on the data to be corrected and the initial corrected data to generate a weighted result, and the two weighted results are fused to generate target-corrected k-space data; the target-corrected k-space data is reconstructed , generating an artifact-corrected target-corrected magnetic resonance image. Through the above technical solutions, it is achieved that the resolution and signal-to-noise ratio of the magnetic resonance image after artifact correction are kept basically unchanged, and the scanning time is not increased, and a magnetic resonance image with better artifact correction effect is obtained.

Description

technical field [0001] Embodiments of the present invention relate to medical image processing technologies, and in particular, to a magnetic resonance image processing method, device, storage medium, and magnetic resonance imaging system. Background technique [0002] Magnetic resonance images captured by an imaging system, such as a magnetic resonance imaging (MRI) system, may be represented as magnetic resonance image data in the spatial domain or as magnetic resonance image-related data in k-space, ie, the frequency domain. Sharp transitions in magnetic resonance images, such as transitions near the boundaries of organs, can be demonstrated in k-space using relatively high frequency components. However, limited sampling time or poor signal-to-noise ratio (SNR) may lead to undersampling of magnetic resonance image-related data in k-space (k-space data for short). This can lead to insufficient high-frequency components in the MR image data, leading to a phenomenon called ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R33/58G06T11/00
CPCG01R33/58G06T11/008
Inventor 李国斌刘楠黄小倩廖术
Owner SHANGHAI UNITED IMAGING INTELLIGENT MEDICAL TECH CO LTD
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