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Magnetic resonance parameter matching method, device and medical image processing equipment

A magnetic resonance and image technology, applied in the field of image processing, can solve the problems of unable to reconstruct the image, unable to ideally represent the image sparsely, etc., to achieve the effect of reducing sampling time, good sparse representation results, and reducing the amount of sampling

Active Publication Date: 2013-05-01
深圳市国创汇康医疗器械科技有限公司
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Problems solved by technology

[0005] The purpose of the embodiment of the present invention is to provide a magnetic resonance parameter matching method, which aims to solve the existing non-adaptive fixed sparse transformation, which cannot ideally represent all images sparsely, resulting in the inability to perform at a higher subsampling rate The problem of accurately reconstructing images

Method used

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  • Magnetic resonance parameter matching method, device and medical image processing equipment
  • Magnetic resonance parameter matching method, device and medical image processing equipment
  • Magnetic resonance parameter matching method, device and medical image processing equipment

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Embodiment 1

[0039] figure 1 It shows the flow chart of the realization of the magnetic resonance parameter acquisition method provided by Embodiment 1 of the present invention, which is described in detail as follows:

[0040] In S101, acquire an image to be optimized;

[0041] In S102, input the image into a preset image reconstruction model, the preset image reconstruction model is a reconstruction model generated according to non-adaptive sparse transformation and adaptive dictionary learning;

[0042] In this embodiment, any non-adaptive sparse transformation, such as wavelet transformation, principal component transformation analysis transformation (PCA transformation) or full variation transformation (TV transformation), etc., can be adapted to the reconstruction model

[0043] In S103, process the image according to the reconstruction model to generate a reconstructed image;

[0044] In S104, the reconstructed image is fitted to obtain magnetic resonance parameters.

[0045] Opt...

Embodiment 2

[0052] image 3 It shows a flow chart of the implementation of the magnetic resonance parameter acquisition method provided by Embodiment 2 of the present invention, which is described in detail as follows:

[0053] In S301, the k-p space is under-sampled to obtain L parameter coding dimensions p={p 1 ,p 2 ,...,p L} undersampled k-space signal

[0054] In this embodiment, the k-p space is under-sampled to obtain L parameter coding dimensions p={p 1 ,p 2 ,...,p L} on the corresponding k-space signal in Its corresponding vector form is y={y 1 ,y 2 ,...,y L},

[0055] In S302, for the L k-space signals Perform reconstruction and generate L temporary reconstructed images The reconstructed image as an image to be optimized;

[0056] In this example, from Reconstruct a series of images in each parameter encoding dimension in The corresponding vector form is x={x 1 ,x 2 ,...,x L}, Wherein, the reconstruction method includes various linear or nonlinea...

Embodiment 3

[0101] Figure 4 It shows the structural diagram of the magnetic resonance parameter acquisition system provided by the third embodiment of the present invention. For the convenience of description, only the parts related to the embodiment of the present invention are shown. The device can be a software unit built in medical image processing equipment, A hardware unit or a combination of hardware and software.

[0102] The system includes: an acquisition unit 41 , an input unit 42 , a generation unit 43 and a fitting unit 44 .

[0103] An acquisition unit 41, configured to acquire an image to be optimized;

[0104] The input unit 42 is configured to input the image into a preset image reconstruction model, the preset image reconstruction model is a reconstruction model generated according to non-adaptive sparse transformation and adaptive dictionary learning;

[0105] A generating unit 43, configured to process the image according to the reconstruction model to generate a re...

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Abstract

The invention is applicable to the technical field of image processing, and provides a magnetic resonance parameter matching method and device and medical image processing equipment. The method comprises the following steps that an image to be optimized is acquired; the image is input into a preset image rebuilding model, wherein the preset image rebuilding model is generated according to non-self-adaptive sparse transformation and self-adaptive dictionary study; the image is processed according to the rebuilding model, and a rebuilt image is generated; and the rebuilt image is fitted, and magnetic resonance parameters are acquired. According to the magnetic resonance parameter matching method, the device and the medical image processing equipment, self-adaptive library study is carried out in the fixed non-self-adaptive transform domain of the image, so that a better sparse expression result is generated while the sampling size and the sampling time are greatly reduced, the precision in rebuilding an original signal from an extremely sparse signal is improved, and the matching of preciser magnetic resonance parameters is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a magnetic resonance parameter matching method, device and medical image processing equipment. Background technique [0002] In recent years, people have paid more and more attention to the application of quantitative magnetic resonance parameters to improve the diagnosis and treatment effect, but because the parameter matching needs to obtain a large number of images in different parameter encoding dimensions, which leads to a long sampling time, although it can be used for The signal is under-sampled to reduce the sampling time, however, how to accurately estimate the parameters through the under-sampled signal is a challenging problem. [0003] Compressed sensing theory provides an effective method to solve this problem. Compressed sensing theory shows that the original signal can be accurately reconstructed by using a small amount of linear measurement of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00
Inventor 梁栋王圣如刘新郑海荣
Owner 深圳市国创汇康医疗器械科技有限公司
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