Non-partial regularization prior reconstruction method for low-dosage X-ray captive test (CT) image

Abstract

The invention discloses a non-partial regularization prior reconstruction method for a low-dosage X-ray captive test (CT) image. The method comprises the following steps of: (1) acquiring a previously scanned standard dosage image of a patient by X-ray CT imaging equipment; (2) acquiring CT projection data of the patient by the X-ray CT imaging equipment under a Low-mAs scanning protocol, and simultaneously acquiring a corresponding correction parameter and a system matrix; (3) constructing a math model for image reconstruction according to statistical distribution met by the projection data acquired in the step (2); (4) constructing a non-partial regularization prior guided by the previously scanned standard dosage image in the step (1), performing model transformation by adopting a maximum posterior estimation method, and constructing a target function for image reconstruction according to the math model obtained in the step (3); and (5) calculating the target function for CT image reconstruction, which is constructed in the step (4), by adopting an iteration algorithm to finish image reconstruction. By the method, the low-dosage CT image can be reconstructed under the Low-mAs scanning protocol.

Description

technical field [0001] The invention relates to an image reconstruction method of medical images, in particular to a non-local regularized prior reconstruction method of low-dose X-ray CT images. Background technique [0002] The quality of X-ray CT images is closely related to the radiation dose. The higher the radiation dose, the better the image quality. However, excessive X-ray exposure can induce cancer, leukemia or other genetic diseases. Obtaining the best CT imaging diagnostic effect with the least dose is one of the important goals in the field of CT imaging research. [0003] When other scanning parameters remain unchanged, low-dose CT imaging can be achieved by directly reducing X-ray low milliampere-seconds (Low-mAs). However, at this time, a large amount of noise will be introduced into the detection data, which will directly lead to serious degradation of the reconstructed image. , and then affect the clinical diagnosis. A large number of experiments have pro...

AI Technical Summary

Problems solved by technology

However, the traditional statistical modeling method only introduces the constraints of the local neighborhood of the image itself as prior information, and fails to consider the prior information that can be provided by the previous scan images of the same patient. The pixel grayscale information cannot effectively distinguish edge information and noise, resulting in over-smoothing or ladder-like effects in the reconstructed image

Images