Cosine transform-based method for adaptive threshold filtering reconstruction of projection data of X-ray computed tomography (CT) medical image

Abstract

The invention discloses a cosine transform-based method for adaptive threshold filtering reconstruction of projection data of an X-ray computed tomography (CT) medical image. The method comprises the steps of (1) using a CT device to collect the projection data on the condition of a low tube current scanning protocol, and obtaining relevant imaging parameters; (2) estimating secondary moment statistical characteristics of the projection data; (3) conducting gray-scale feature-based adaptive segmentation on the collected projection data; (4) conducting discrete cosine transform on the projection data subjected to segmentation in Step (3); (5) conducting data threshold filtering modeling on the projection data subjected to discrete cosine transform in Step (4) by using projection data variances calculated in Step (2); (6) conducting inverse discrete cosine transform on filtered data obtained in Step (5), and obtaining the filtered projection data before reconstruction; and (7) for the filtered projection data obtained in Step (6), using an analytic reconstruction method to achieve reconstruction of the CT image. By the aid of the method, high-quality reconstruction of the CT image can be achieved.

Description

technical field [0001] The patent of the present invention relates to an image reconstruction method of medical images, specifically an adaptive threshold filtering reconstruction method based on cosine transform of X-ray CT medical image projection data. Background technique [0002] The high radiation dose in X-ray CT imaging has potential risks to the human body, causing radiation damage and inducing malignant tumors. Minimizing the use of X-ray doses is the focus of attention in the field of medical X-ray CT imaging. [0003] Although reducing the tube current (mA) in CT scanning can directly reduce the radiation dose of X-rays, the corresponding imaging data will contain a large amount of random quantum noise, which directly leads to serious degradation of image quality and is difficult to use in clinical diagnosis. Therefore, achieving high-quality reconstruction of CT images under low tube current (Low-mA) scanning protocol is one of the key technologies in the field...

AI Technical Summary

Problems solved by technology

[0003] Although reducing the tube current (mA) in CT scanning can directly reduce the radiation dose of X-rays, the corresponding imaging data will contain a large amount of random quantum noise, which directly leads to serious degradation of image quality and is difficult to use in clinical diagnosis.

Images