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Image reconstruction method, device, equipment and medium

A technology of image reconstruction and target image, which is applied in the field of image reconstruction, can solve the problems of inability to effectively ensure the consistency of image reconstruction data and fixed models, and achieve the effect of ensuring data consistency, improving segmentation accuracy, and improving network freedom

Pending Publication Date: 2020-01-24
SHENZHEN INST OF ADVANCED TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing deep neural network model is relatively fixed, which may not be able to effectively guarantee the data consistency in the image reconstruction process

Method used

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  • Image reconstruction method, device, equipment and medium
  • Image reconstruction method, device, equipment and medium
  • Image reconstruction method, device, equipment and medium

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Embodiment 1

[0026] figure 1 It is a flowchart of an image reconstruction method provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of performing image reconstruction on the acquired under-sampled K-space data. The method can be executed by an image reconstruction device, and the image reconstruction device can be implemented in software and / or hardware, for example, the image reconstruction device can be configured in a computer device. Such as figure 1 As shown, the method includes:

[0027] S110. Obtain the collected under-sampled data, and input the under-sampled data into a pre-trained target image reconstruction model, wherein the target image reconstruction model is a generalization of the data fidelity item of the original image reconstruction model obtained after solving.

[0028] In this embodiment, the collected under-sampled data is learned through a machine learning algorithm to obtain a reconstructed image corresponding to th...

Embodiment 2

[0034] Figure 2a It is a flowchart of an image reconstruction method provided by Embodiment 2 of the present invention. This embodiment is optimized on the basis of the foregoing embodiments. Such as Figure 2a As shown, the method includes:

[0035] S210. Generalize the data fidelity item in the original image reconstruction model to obtain a generalized image reconstruction model.

[0036] In this embodiment, the original image reconstruction model is an image reconstruction model used in a traditional deep learning-based MRI image reconstruction method. The data fidelity item in the original image reconstruction model defines the relationship among the encoding matrix, the image to be reconstructed, and the undersampled K-space data. Generally, the data fidelity item in the original image reconstruction model is characterized by using the 2 norm between the reconstructed K space and the sampling point, but the least square constraint of the above characterization is ba...

Embodiment 3

[0055] image 3 It is a schematic structural diagram of an image reconstruction device provided by Embodiment 3 of the present invention. The image reconstruction device can be realized by software and / or hardware, for example, the image reconstruction device can be configured in a computer device. Such as image 3 As shown, the device includes an undersampling data acquisition module 310 and a reconstructed image acquisition module 320, wherein:

[0056] The undersampling data acquisition module 310 is configured to acquire the acquired undersampling data, and input the undersampling data into the pre-trained target image reconstruction model, wherein the image reconstruction model is the data of the original image reconstruction model The fidelity item is obtained after generalization;

[0057] The reconstructed image acquisition module 320 is configured to acquire the image output by the target image reconstruction model.

[0058] In the embodiment of the present invent...

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Abstract

The embodiment of the invention discloses an image reconstruction method and device, equipment and a medium. The method comprises: acquiring collected under-sampling data, inputting the under-samplingdata into a pre-trained target image reconstruction model, and obtaining the target image reconstruction model by generalizing a data fidelity item of an original image reconstruction model and thensolving the data fidelity item; and obtaining a reconstructed image output by the target image reconstruction model. According to the image reconstruction method provided by the embodiment of the invention, the data fidelity item of the original image reconstruction model is generalized and then solved to obtain the target image reconstruction model. According to the method, the reconstructed image is obtained based on the obtained target image reconstruction model and the undersampled data, the network freedom degree of the neural network is improved, the data consistency in the image reconstruction process is guaranteed, and the quality of the reconstructed image is improved.

Description

technical field [0001] Embodiments of the present invention relate to the field of image reconstruction, and in particular, to an 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 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. How to improve the scanning speed and reduce the scanning time while the imaging quality is clinically acceptable is particularly important. [0003] In terms of fast imaging, MRI image reconstruction using deep learning methods has received increasing attention. The deep learning method uses the neural network to learn the optimal parameters required for reconstruction from a large amount of training data or direc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00G06K9/00
CPCG06T11/003G06T2210/41G06V20/49
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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