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A finite angle projection reconstruction method based on L0 norm and singular value threshold decomposition for double-regular-term optimization

A norm optimization and singular value technology, applied in the field of image processing, can solve the problems of missing details and edges, and achieve the effect of restoring image contours and details, reducing artifacts, improving quality and practicability

Active Publication Date: 2019-04-30
CHONGQING UNIV
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Problems solved by technology

[0004] In view of this, the object of the present invention is to provide a finite-angle projection reconstruction method based on double regular term optimization of L0 norm and singular value threshold decomposition, which is used to solve the problems of existing finite-angle projection reconstruction image detail and edge loss, Thereby improving the reconstruction quality of CT images

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  • A finite angle projection reconstruction method based on L0 norm and singular value threshold decomposition for double-regular-term optimization
  • A finite angle projection reconstruction method based on L0 norm and singular value threshold decomposition for double-regular-term optimization
  • A finite angle projection reconstruction method based on L0 norm and singular value threshold decomposition for double-regular-term optimization

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Embodiment 1

[0056] In order to better reflect the advantages of a finite-angle projection reconstruction algorithm based on the L0 norm and singular value threshold decomposition double regular term optimization of the present invention in terms of reconstruction effect, the following will combine the specific embodiments described in the present invention The algorithm is compared with the existing SART algorithm, singular value threshold decomposition (SVT) regularization algorithm, and gradient L0 norm regularization algorithm.

[0057] In practical applications, projection data usually inevitably contain noise. Therefore, in order to verify the validity and stability of the reconstruction algorithm of the present invention, such as figure 2 As shown, the ideal image of the reconstructed Shepp-Logan model is selected, and Gaussian noise with a mean of zero and a standard deviation of 0.4% of the maximum projection data is superimposed on the projection data of the selected Shepp-Logan...

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Abstract

The invention relates to a finite angle projection reconstruction method based on double regular item optimization of L0 norm and singular value threshold decomposition, and belongs to the field of image processing. The method specifically comprises the following steps: S1, establishing an optimization problem target equation according to a CT imaging principle, a regularization framework and a projection data set P; S2, initializing parameters; S3, performing iteration by adopting a SART algorithm to obtain an image X, and correcting the X through error feedback; S4, performing gradient L0 norm optimization on the corrected image X to obtain an image XL0, and updating an error d1; S5, performing singular value decomposition on the image optimized in the step S4, adding a soft threshold constraint to optimize the image to obtain an XSVT, and updating an error d2; and S6, carrying out next round of iteration on the image obtained in the step S5 according to the step S3 until an iteration termination condition is met. According to the method, the CT image contour can be effectively recovered, finite angle artifacts are reduced, and therefore the finite angle CT imaging quality and applicability are improved.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a limited angle projection reconstruction method based on double regular term optimization of L0 norm and singular value threshold decomposition. Background technique [0002] Computed tomography (CT) uses the collected projection data and a certain reconstruction algorithm to reconstruct the density distribution image of the measured object by using the X-ray attenuation information back projection, which has the advantages of non-destructive, high-precision and visualization, so It is widely used in medical imaging, industrial non-destructive testing and safety inspection and other fields. In the case of complete projection data, that is, complete angle projection, good reconstruction results can be obtained by using iterative reconstruction algorithm and analytical reconstruction algorithm. However, in the actual CT scanning process, due to the influence of external factors such...

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Application Information

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IPC IPC(8): G06T3/00
CPCG06T3/08
Inventor 王珏蔡玉芳傅范平张秀英朱斯琪
Owner CHONGQING UNIV
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