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Compressed sensing low-field magnetic resonance imaging algorithm

A magnetic resonance imaging and compressed sensing technology, applied in the direction of measuring magnetic variables, measuring devices, instruments, etc., can solve problems such as inability to sparsely represent two-dimensional signals-Vitch outliers, difficulty in capturing image contours, and transfer sensitivity. Achieve the effect of overcoming aliasing artifacts, improving imaging speed, and good robustness

Inactive Publication Date: 2018-10-30
UNIV OF SHANGHAI FOR SCI & TECH
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Problems solved by technology

For example, Discrete Wavelet Transform (DWT) has three disadvantages: it has transfer sensitivity, lack of directionality, lack of phase information, and DWT cannot sparsely represent a one-dimensional signal of a two-dimensional signal, and it is difficult to capture regular image contours

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  • Compressed sensing low-field magnetic resonance imaging algorithm
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Embodiment Construction

[0034] Such as figure 1 As shown, a compressed sensing (low field) magnetic resonance imaging algorithm combining dual-tree wavelet transform and wavelet tree sparseness includes the following steps:

[0035] 1. Read the raw data files collected by the low-field nuclear magnetic resonance equipment;

[0036]2. The original data is used as full-sampled K-space data, and the simulated under-sampling processing operation is performed to obtain randomly variable density under-sampled K-space data;

[0037] 3. The sparser the magnetic resonance image itself or in a certain transformation domain, the better its reconstruction quality. The combination of dual-tree wavelet transform and wavelet tree sparseness is used as the sparse transformation in compressed sensing theory;

[0038] 4. Combine the undersampled K-space data obtained in step 2 with the compressive sensing theory in step 3 to build a model, and model the MRI image reconstruction problem as a linear model that includes...

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Abstract

The invention relates to a compressed sensing low-field magnetic resonance imaging algorithm. The algorithm comprises the steps that collected low-field magnetic resonance original data is subjected to undersampling, and random variable density K spacial data is obtained; undersampled K spacial data and a compressed sensing theory are combined for modeling, the modeling problem becomes a linear combination minimization problem containing a data fidelity term, a sparse prior term and a total variation term, and dual tree wavelet transformation and wavelet tree sparse joint serve as sparse transformation in the compressed sensing theory; by means of the dual tree wavelet transformation and wavelet tree sparse joint compressed sensing magnetic resonance imaging algorithm, solution is conducted, and a low-field magnetic resonance image rebuilt by the undersampled variable density K spacial data is obtained. Dual tree wavelet transformation is introduced to serve as sparse transformation ina CS-MRI model, and the defect that due to the fact that traditional wavelet transformation has sensibility and shift variant, lacks direction and has a rebuilding image, aliasing artifacts are caused is overcome. The computing accuracy is high, robustness is good, and the signal-to-noise ratio of the rebuilding image is increased while the imaging speed is increased.

Description

technical field [0001] The invention relates to a nuclear magnetic resonance imaging technology, in particular to a compressed sensing low-field magnetic resonance imaging algorithm combining dual-tree wavelet transform and wavelet tree sparse combination. Background technique [0002] In the world, magnetic resonance technology is developing rapidly and has been widely developed in many fields. According to the different background intensity, magnetic resonance technology can be divided into three categories: high-field, medium-field, and low-field. ≤0.5T (T, Tesla). The magnetic source of high-field magnetic resonance technology mostly uses superconducting magnets to generate high-intensity magnetic fields, and the collected signals have strong amplitude and high signal-to-noise ratio. However, superconducting magnets have high requirements on the equipment environment (for example, they must be used under liquid nitrogen cooling conditions) and are very expensive to pur...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R33/58
CPCG01R33/58
Inventor 柴青焕苏冠群侯学文常晓聂生东
Owner UNIV OF SHANGHAI FOR SCI & TECH
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