CT sparse projection image reconstruction method and CT sparse projection image reconstruction device at limited sampling angle

Abstract

The invention discloses a CT sparse projection image reconstruction method and a CT sparse projection image reconstruction device at a limited sampling angle. The method comprises the steps of obtaining a pseudo-inverse matrix of a projection equation according to projection data, generating a random solution set according to the pseudo-inverse matrix, reserving or replacing each solution in the current random solution set correspondingly, selecting an optimal solution from the current random solution set when the number of iterations reaches a preset maximum value, and adopting the selected optimal solution as a to-be-obtained reconstruction result. The device comprises a memory for storing at least one program and a processor for loading at least one program to execute the method of theinvention. According to the method, the pseudo-inverse of a discretized projection reconstruction equation is used as an initial solution of the algorithm, so that the quality of the initial solutionis guaranteed. A set of solutions are generated through random walk, and then the iterative optimization is carried out respectively. The diversity of optimization paths is guaranteed. Troubles causedby the defects of an initial solution and an iteration path of a traditional reconstruction method can be overcome. The method and the device are applied to the technical field of image processing.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a CT sparse projection image reconstruction method and device under a limited sampling angle. Background technique [0002] Explanation of terms: [0003] CT: Computed Tomography [0004] TV: Total Variation [0005] POCS-TV: Projection On Convex Sets-Total Variation (convex set projection-total variation minimization algorithm) [0006] CT imaging is to reconstruct the tomographic image of the object by using the intensity attenuation information that occurs when X-rays penetrate the object to be detected. Improving the reconstruction quality of sparse projection data under limited angle is a very important and urgent problem in the field of CT reconstruction. For example, in medicine, when scanning the human brain, excessive radiation dose may cause secondary damage to the brain, so it is necessary to reduce the radiation dose of CT radiation, and only sparse proje...

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Problems solved by technology

[0007] However, due to the extremely low symmetry and completeness of the sparse projection data under the limited sampling angle, the search space of the reconstruction algorithm is huge. The traditional iterative reconstruction method based on convex optimization starts from a single initial solution, according to the objective function in the current solution. On the one hand, the reconstruction quality is seriously affected by the quality of the initial solution. On the other hand, the single fixed iterative path makes the optimization process unable to traverse the huge search space in a limited time, and it is easy to fall into local optimum and cannot jump. away, the reconstructed image quality is poor

It can be seen that the difficulties faced by the existing reconstruction methods are mainly caused by the initial solution and the iterative path

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