Fast iterative magnetic resonance image reconstruction method based on high-order total variation regularization

A magnetic resonance image, full variation technology, applied in image enhancement, image analysis, image data processing, etc.

Active Publication Date: 2017-03-22
黑龙江省工研院资产经营管理有限公司
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[0005] The purpose of the present invention is to provide a fast iterative magnetic resonance image reconstruction method based on high-order full variation regularization that can simultaneously improve the reconstruction image quality and computational efficiency in view of the deficiencies of the current magnetic resonance image reconstruction method

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  • Fast iterative magnetic resonance image reconstruction method based on high-order total variation regularization
  • Fast iterative magnetic resonance image reconstruction method based on high-order total variation regularization
  • Fast iterative magnetic resonance image reconstruction method based on high-order total variation regularization

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[0085] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0086] Such as figure 1 Shown, the specific implementation steps of the present invention are as follows:

[0087] (1) Utilize the pre-set subsampling template to obtain part of the k-space data, in order to verify the effect of the present invention, four groups of reference magnetic resonance images are adopted, such as figure 2 Shown are the lateral brain MRI image (a), axial brain MRI image (b), angiography MRI image (c), wrist MRI image (d), and the reference image Liye transform, collect the original k-space data, the collected under-sampled k-space data is expressed as b=Af+n, where A is the under-sampling operation operator in k-space after performing Fourier transform on the magnetic resonance image, n is the additive noise that may exist in actual sampling, b is the obtained k-space undersampling data, and f is the image to be reconstruct...

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Abstract

The invention relates to a fast iterative magnetic resonance image reconstruction method based on high-order total variation regularization, relates to the technical field of magnetic resonance imaging, and improves reconstructed image quality and computational efficiency. The method comprises steps of: (1) acquiring partial k spatial data; (2) establishing a magnetic resonance image reconstruction model; (3) directly performing inverse Fourier transform on the partial k spatial data to obtain a spatial-domain prediction magnetic resonance image as an initial reconstruction image; (4) performing fast iterative solution of the reconstruction model; (5) obtaining the magnetic resonance reconstruction image of this iteration; (6) determining whether the current reconstruction image result satisfies the convergence condition; (7) increasing the value of an iterative parameter and using the updated magnetic resonance image in the current iteration step as the initial reconstruction image, and returning to the step (5) to continue the cyclic iteration operation. Compared with a total variation method, an image high-order derivative Laplacian method, a wavelet method and the like, the method can obtain the high-quality reconstructed image and improve the reconstruction speed.

Description

technical field [0001] The invention relates to the technical field of magnetic resonance imaging, in particular to an augmented Lagrangian fast iterative magnetic resonance image reconstruction method based on a high-order full variation regularization model under the compressed sensing theory. Background technique [0002] Magnetic resonance imaging plays an important role in medical detection due to its advantages of non-invasiveness and no ionizing radiation. However, problems such as long scanning time hinder the development of MRI. Compressed sensing theory utilizes the sparsity of the image to reconstruct the magnetic resonance image through a nonlinear algorithm in the case of collecting some k-space samples, so as to achieve the goal of accelerating magnetic resonance imaging. [0003] In the MRI image reconstruction algorithm based on compressed sensing theory, the common method is the regularization method, that is, the image reconstruction problem is reduced to ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/003G06T2207/10088G06T2207/20056
Inventor 胡悦仲崇潇卢鑫赵旷世
Owner 黑龙江省工研院资产经营管理有限公司
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