Fast CT reconstruction method of multi-scale sparse projection data

Abstract

The invention discloses a fast CT reconstruction method of multi-scale sparse projection data. The method comprises the specific steps of collecting sparse angle projection data; carrying out down-sampling calculation by using original projection data and a corresponding projection matrix; reconstructing the original projection data by using a convex optimization method; reconstructing obtained down-sampling projection data by using a convex optimization iteration method and a constraint of a regularization term; fusing the reconstruction result; and utilizing a reconstruction algorithm for reconstruction again by employing the fused image as an initial value. According to the method, the sparse angle projection data is collected, reconstruction is carried out on an existing regularization constraint-based convex optimization algorithm, and the speed of the reconstruction algorithm is increased through fusing two reconstruction results on the basis of ensuring the original algorithm reconstruction quality, so that fast CT reconstruction of the multi-scale sparse projection data is achieved. The fast CT reconstruction method of the multi-scale sparse projection data can be widely used for the technical field of CT image reconstruction.

Description

technical field [0001] The invention relates to the technical field of CT image reconstruction, in particular to a fast CT reconstruction method of multi-scale sparse projection data. Background technique [0002] CT (Computed Tomography) reconstruction technology is an important technology widely used in the field of medical aided diagnosis and industrial non-destructive testing. It has advantages in non-destructive testing and visualization. The sparse angle projection data has the advantages of fast scanning speed and less radiation exposure of the object to be detected, so it is widely used. However, as the irradiation angle of rays decreases, less data can be used for reconstruction, which also makes the reconstruction effect worse and the reconstruction speed slower, which seriously affects the development of CT reconstruction technology. In order to increase the speed of reconstruction and ensure the quality of reconstruction, it is necessary to use algorithms to red...

AI Technical Summary

Problems solved by technology

Since the analytical method requires complete projection data for reconstruction, the analytical method does not work well when the projection angle is sparse

[0003] Although the iterative method shows better robustness in reconstruction, it cannot be compared with the analytical method in terms of reconstruction speed.

Especially when regularization constraints are introduced as constraints, the reconstruction time is also greatly increased

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