Low-dose CT image statistical reconstruction method based on discrete shear wave regularization

Abstract

The invention discloses a low-dose CT image statistical reconstruction method based on discrete shear wave regularization. Coefficient weight meeting the data statistical property is added in a data fidelity term so as to reduce influence of noise on a reconstruction result, and prior information of an image which can be sparsely expressed in a shear wave domain acts as a regularization term to be added in a target function by utilizing an augmented Lagrange method. The regularization coefficient self-adaptively changes according to error so that robustness of the algorithm is further enhanced. The reconstructed image with clear retaining of structural details can still be obtained when radiation dose is reduced to be 10% of that of an FBP algorithm or even lower. The low-dose CT image statistical reconstruction method based on discrete shear wave regularization is the low-dose CT image statistical iterative reconstruction method suitable for the two conditions of less view angle and low tube current scanning.

Description

technical field [0001] The invention relates to the image statistical reconstruction technology of low-dose CT, more specifically, it is a CT image statistical reconstruction method for projection with few angles of view or low X-ray tube current projection. Background technique [0002] Due to the characteristics of fast, accurate, non-invasive, and painless, CT technology is increasingly used in clinical diagnosis. However, during the transmission process of X-rays, part of the energy will be transferred to the human body, causing physical damage and even cancer. In recent years, due to the widespread use of CT, radiation risks have attracted more and more attention, and the issue of low-dose CT has gradually become a research hotspot. [0003] There are two common ways to reduce the CT dose. One is to reduce the current intensity of the X-ray tube to reduce the exposure dose at each viewing angle. Currently, in order to obtain high-quality CT reconstruction images clini...

AI Technical Summary

Problems solved by technology

First of all, TV minimization only constrains the size of the sum of the discrete gradient transformations of the image reconstructed by the data fidelity item, which belongs to a smoothness constraint with better edge preservation ability, but the ability to express image features is limited

Therefore, when sparsity constraints are applied to it, it is easy to cause loss of signal in the case of noise elimination

Secondly, the TV minimization constraint is based on the smoothness of the image slices, and the actual CT image cannot accurately meet this condition. In the CT reconstruction of incomplete data based on the TV minimization constraint, the reconstruction results often have unclear image edges. , poor ability to express small structures, block artifacts under high noise, etc.

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