Compressed sensing magnetic resonance imaging method based on iterative p-threshold projection algorithm

A magnetic resonance imaging and projection algorithm technology, which is applied in projection reconstruction, 2D image generation, calculation, etc., can solve the problems of low precision, slow convergence speed of threshold function, and high imaging error, so as to improve imaging speed and improve imaging The effect of low accuracy and reconstruction error

Active Publication Date: 2020-12-29
DALIAN UNIV
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However, the threshold function used by the fast iterative soft-threshold projection algorithm has slow convergence speed and low precision, and cannot impose greater penalties on small coefficients, resulting in high fina...

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  • Compressed sensing magnetic resonance imaging method based on iterative p-threshold projection algorithm
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  • Compressed sensing magnetic resonance imaging method based on iterative p-threshold projection algorithm

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[0046]In order to make the technical solutions and advantages of the present invention clearer, the following describes the technical solutions in the embodiments of the present invention clearly and completely in conjunction with the drawings in the embodiments of the present invention:

[0047]A compressed sensing magnetic resonance imaging method based on an iterative p-threshold projection algorithm is characterized in that it comprises the following steps:

[0048]S1: Fourier transform the T2-weighted brain map, transform the Fourier-transformed T2-weighted brain map to K-space, obtain the K-space data of the T2-weighted brain map, and sample the K-space data of the T2-weighted brain map in a non-linear manner The template is adopted to obtain the T2-weighted brain map under-sampled K-space data; the brain map is an image obtained by scanning the head of a healthy volunteer with a 3T Siemens Trio Tim magnetic resonance imager;

[0049]S2: Perform redundant transformation on the under-sa...

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Abstract

The invention discloses a compressed sensing magnetic resonance imaging method based on an iterative p-threshold projection algorithm, and belongs to the field of magnetic resonance imaging. The method comprises the following steps of: performing Fourier transform on a T2 weighted brain graph, transforming the T2 weighted brain graph subjected to Fourier transform into a K space to obtain K spacedata of the T2 weighted brain graph, and adopting the K space data of the T2 weighted brain graph according to a nonlinear sampling template to obtain T2 weighted brain graph under-sampling K space data; carrying out redundancy transformation on the T2 weighted brain graph undersampling k space data, and carrying out sparse representation on the T2 weighted brain graph undersampling k space data;and carrying out image reconstruction on the sparsely represented T2 weighted brain map undersampling k space data based on an iterative p-threshold projection algorithm or a fast iterative p-threshold projection algorithm, designing a new sparse target function by flexibly changing a p value, and achieving a better reconstruction effect .

Description

Technical field[0001]The invention relates to the field of magnetic resonance imaging, in particular to a compressed sensing magnetic resonance imaging method based on an iterative p-threshold projection algorithm.Background technique[0002]Magnetic resonance imaging (Magnetic Resonance Imaging, MRI) plays an important role in clinical diagnosis. Although MRI can provide high-quality images with excellent soft tissue contrast information, its imaging speed is not satisfactory and is mainly affected by physical (such as The amplitude and rate of change of the gradient pulse) and physiological (neural stimulation) constraints. The reconstruction model under the redundant system aims to recover the original data from the incomplete observation data using prior information. At present, related work is mainly based on two aspects: decomposition model and comprehensive model reconstruction. For the solution of comprehensive models, iterative soft-thresholding algorithms (ISTA) are widely u...

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Application Information

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IPC IPC(8): G06T11/00
CPCG06T11/006G06T11/008G06T2211/424Y02A90/30
Inventor 杜秀丽刘晋廷吕亚娜邱少明
Owner DALIAN UNIV
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