The present application relates to a kind of low-
dose X-
ray CT image-oriented
noise removal method, by the method of semi-supervised contrast learning, low-
dose CT image is removed
noise by teacher-student consistency network and prior
knowledge transfer method, reduce effective
information loss, retain more detail information, to provide high-quality input data for subsequent image reconstruction, realize the reconstruction of high-quality CT image under low-
dose X-
ray condition.Compared with the traditional method based on low-pass filter or image statistics, the present application can better remove
noise, so that the image after denoising is more real and clear, and by adjusting its
network structure and
hyperparameter, it can be applied to various low-dose CT
image denoising tasks, including different scanning
modes, different organs, etc., with high versatility and
scalability.