Multi-scale feature generative adversarial network for suppressing artifact noise in low-dose CT image

Abstract

The invention belongs to the technical field of CT imaging, and adopts the specific scheme that a multi-scale feature generative adversarial network for suppressing artifact noise in a low-dose CT image is adopted, an LDCT image noise reduction model is selected, an LDCT image and NDCT image data set is constructed, the LDCT image is input into an error feedback pyramid generator network, he pyramid generator network extracts cross-scale features of the LDCT image from different angles, the LDCT image is processed by the error feedback pyramid generator network and then a preliminary noise reduction result graph is output, the NDCT image and the preliminary noise reduction result graph are jointly input into an interleaved convolution discriminator sub-network for iterative training, and afinal noise reduction result graph is output; the error feedback pyramid generator can extract shallow features and deep features in the same scale of the image, increase the richness of feature extraction, improve the discrimination capability of the discriminator, and the multi-scale feature generative adversarial network solves the problem of under-noise reduction or over-noise reduction caused by high similarity between noise artifacts and tissue structure distribution.

Description

technical field [0001] The invention belongs to the technical field of CT imaging, and discloses a deep learning method for efficiently suppressing noise and artifacts in low-dose CT images and facilitating accurate medical diagnosis and analysis in the later stage. Background technique [0002] Since the advent of Computed Tomography (CT) technology in the 1970s, it has been widely used in industrial and agricultural production, safety inspection, biomedical imaging, and industrial nondestructive imaging due to its advantages of simple operation, fast imaging speed, and high sensitivity. Detection, geology and other fields. In the field of medical diagnosis and treatment, CT images have the advantages of clear imaging, high density resolution, and the ability to clearly display three-dimensional information of images, so they are widely used in various congenital developmental abnormalities, inflammatory diseases, metabolic lesions, traumatic changes, Benign and malignant ...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the technical problems of large network complexity, many network parameters, and unstable network training process in the prior art, the present invention provides a deep learning method that can realize fast and efficient LDCT image noise reduction. Under the premise of network complexity and computing time, better artifact noise suppression and detail preservation effect can be achieved

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