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Generalized tree sparse-based weight nuclear norm magnetic resonance imaging reconstruction method

A magnetic resonance imaging and nuclear norm technology, applied in the field of medical image processing, can solve the problems of not taking into account the inherent structure sparsity of the signal, the difficulty of accurately reconstructing magnetic resonance MRI images, and the difficulty of medical diagnosis, so as to achieve less aliasing artifacts , Fast recovery speed, good fidelity effect

Active Publication Date: 2017-05-31
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

However, the existing CS-MRI image reconstruction methods mainly use the sparsity of the magnetic resonance MRI image to reconstruct the image, and do not take into account the inherent structural sparsity of the signal, so it is difficult to accurately reconstruct the original real magnetic resonance MRI image. make medical diagnosis difficult

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  • Generalized tree sparse-based weight nuclear norm magnetic resonance imaging reconstruction method
  • Generalized tree sparse-based weight nuclear norm magnetic resonance imaging reconstruction method
  • Generalized tree sparse-based weight nuclear norm magnetic resonance imaging reconstruction method

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Embodiment Construction

[0045] The present invention will be further described below in conjunction with specific embodiments.

[0046] Such as figure 1 As shown, the weighted nuclear norm magnetic resonance imaging (MRI) reconstruction method based on generalized tree sparseness described in this embodiment is specifically: first, obtain a test magnetic resonance imaging (MRI) sampling data sample and perform Fourier transform; then according to the sampling The sparse signal of the tree structure is constructed from the signal, and the sparse expression of the constrained objective function is approximated by the weighted nuclear norm; then the constrained objective function is optimized by the augmented Lagrangian multiplier method and the Alternate Direction Search (ADMM) algorithm The test data is updated iteratively until the estimated restored data is obtained; finally, the final restored image is obtained by constructing tree sparse inverse transformation. It includes the following steps:

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Abstract

The invention discloses a generalized tree sparse-based weight nuclear norm magnetic resonance imaging reconstruction method. The method comprises the following steps of: firstly obtaining a test magnetic resonance imaging sampling data sample to carry out Fourier transform; constructing a sparse of a tree structure according to a sampled signal, and approaching to sparse expression of a constrained target function by utilizing a nuclear norm with a weight; optimizing the constrained target function through an augmented lagrangian multiplier method, and carrying out iterative updating on the test data through an alternating direction search algorithm until estimated recovery data is obtained; and obtaining a final recovery image through constructing tree sparse inverse transformation. According to the method, an internal structure relationship between image signals is sufficiently mined, the generalized tree sparse structure characteristics of image blocks are combined with weight nuclear norms, and the calculation process is simplified by utilizing an ADMM algorithm, so that the complexity of the algorithm is reduced, the performance of a part of spatial data reconstruction images is improved, images can be reconstructed more accurately under less scanning and measurement, fake shadows of the reconstructed images can be decreased and rapid magnetic resonance imaging is realized.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, in particular to a weighted nuclear norm magnetic resonance imaging (MRI) reconstruction method based on generalized tree sparseness, which is mainly used for clear and rapid recovery of medical images, reducing artifacts of reconstructed images, Recover more image details. Background technique [0002] Magnetic resonance imaging (MRI) is widely used in the medical field because of its low damage and high diagnostic significance. Magnetic resonance MRI is based on the principle of magnetic resonance. The basic physical concepts involved in magnetic resonance mainly include the spin and magnetic moment of atoms, the energy state of spin magnetic moment in an external magnetic field, the conditions for generating magnetic resonance, Larmor precession, magnetization vector, and radio frequency field to magnetization vector. and relaxation process. [0003] Traditional magn...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/10088G06T2207/30016G06T2207/20192G06T2207/20056G06T5/73
Inventor 傅予力陈真徐俊伟向友君周正龙
Owner SOUTH CHINA UNIV OF TECH
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