A CT image sparse reconstruction method of an iterative evolution model

Abstract

The invention discloses a CT image sparse reconstruction method of an iterative evolution model. The method comprises the following steps: acquiring sparse projection data with a fixed projection angle range or a projection angle interval reaching a set value; Generating four initial solutions, namely a pseudo-inverse solution, a random value solution, a total 0 value solution and a total 1 valuesolution; obtaiing Four groups of solution sets through random walk; Performing validity constraint on each solution in each solution set; Evaluating each solution in each group of random solution setaccording to a fitness evaluation function; transferring each solution according to a novel iteration evolution model; Updating each solution according to a convex optimization-based iterative algorithm; Selecting the current optimal solution update of each group according to the fitness evaluation; And if so, selecting an optimal solution as a reconstruction result according to the fitness evaluation. The method can improve the quality of the reconstructed image, and reduces the reconstruction artifacts.

Description

technical field [0001] The invention relates to the field of CT image reconstruction, in particular to a CT image sparse reconstruction method of an iterative evolution model. Background technique [0002] Computed Tomography (CT) is based on the phenomenon that X-rays are absorbed and attenuated by the medium when they penetrate the cross-section of the object to be detected, and the received projection data is used to reconstruct the tomographic image of the object. The analytical method of the traditional reconstruction method requires the projection data to meet the data integrity requirements (Tuy-Smith condition), that is, to perform uniform and dense projection within the angle range of 180°, otherwise there will be serious artifacts in the reconstruction results. However, in many application scenarios, only sparse projection data with limited sampling angles and large sampling intervals can be obtained. For example, in brain CT for medical applications, excessive X-...

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Problems solved by technology

On the one hand, the reconstruction quality is seriously affected by the quality of the initial solution. The iterative optimization on a single solution makes the reconstruction process unable to traverse a huge search space in a limited time, and often only obtains a local optimal solution, resulting in poor reconstruction image quality.

[0006] In addition, especially when the projection angle range is limited, on the one hand, the projection data is not symmetrical, so gradients in different directions have different effects on image information recovery , the total variation constraints cannot guarantee the correct global optimal iterative path; on the other hand, the gradient descent method adopted by the POCS-TV algorithm fixes the iterative path in the direction of the negative gradient of the objective function. Under the premise that it is not guaranteed to reach the global optimal iterative path, it is also impossible to jump out of the current direction to find a better solution, and it is impossible to obtain a high-quality solution

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