Sorting structure group nonconvex constraint-based CS-MRI image reconstruction method

A non-convex constraint, image reconstruction technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problem of easy over-fitting and high dictionary complexity, to improve the sparsity, enhance the ability of sparse representation, Good overall visual effect

Active Publication Date: 2017-10-27
CHONGQING UNIV
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Problems solved by technology

Subsequently, learning-based dictionaries (such as KSVD) were used in MRI image reconstruction and achieved good results, but this dic

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  • Sorting structure group nonconvex constraint-based CS-MRI image reconstruction method
  • Sorting structure group nonconvex constraint-based CS-MRI image reconstruction method
  • Sorting structure group nonconvex constraint-based CS-MRI image reconstruction method

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Embodiment Construction

[0028] refer to figure 1 , the present invention is based on the CS-MRI image reconstruction method of the non-convex constraints of the sorting structure group, and the specific steps include the following:

[0029] Step 1. Initially restore the image and establish a structure group corresponding to each image block.

[0030] (1a) Input a piece of MRI original K-space observation data y, use the total variation method to perform initial reconstruction on it, and obtain the initial reconstructed image x (0) ;

[0031] (1b) Divide the image according to the size of The image block is extracted, and for each target image block x i Euclidean distance comparison with other image blocks within the search range;

[0032] (1c) Take out the target image block x i S-1 image blocks with the smallest Euclidean distance, and form a structure group X with the target image block i =[x i,0 ,x i,1 ,...x i,S-1 ], where x i,0 =x i .

[0033] Step 2, since all similar image blocks i...

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Abstract

The invention discloses a sorting structure group nonconvex constraint-based CS-MRI image reconstruction method, and belongs to the technical field of digital image processing. The image reconstruction method utilizes structure group sorting to improve expression abilities of fixed dictionaries and utilizes log-sum norms to carry out nonconvex constraint on structure groups. The method comprises the following steps of: firstly finding a similar image block set, namely, a structure group, of a target image block; establishing a sorting model for the target image block and using an obtained sorting matrix for the sorting the structure group, so as to improve the expression ability, for the structure group, of a fixed dictionary; and finally carrying out nonconvex constraint on sparse coefficients by utilizing the a log-sum norm. According to the method, internal parts of the structure groups are sorted so that the sparse representation performance of structure groups is improved; a rapid threshold value operator is adopted to solve non-convex optimization of the coefficients, so that the estimated coefficients are closer to real values; and through the method, the obtained images are clearer, much detail information of the images is retained and the recovery correctness is higher, so that the method can be used for the reconstruction of medical images.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to a method for image reconstruction by enhancing sparse representation and non-convex constraints in the transformation domain, which is used for high-quality restoration of medical images. Background technique [0002] Magnetic resonance imaging (MRI) is widely used in clinical medical diagnosis due to its high resolution and non-invasiveness. Traditional magnetic resonance imaging requires Nyquist sampling of raw data, which takes a long time and costs high, which limits the application of this technology in medicine to a certain extent. [0003] With the theory of Compressed Sensing (CS) proposed in recent years, MRI has the possibility of a major breakthrough in reducing imaging time. Compressed sensing theory proposes that the sparse characteristics of the signal can be used to reconstruct the sampled signal using a nonlinear algorithm when the sa...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/00G06T2207/10088
Inventor 刘书君曹建鑫沈晓东李正周张奎唐明春
Owner CHONGQING UNIV
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