Shallow residual encoding and decoding recursive network for low-dose CT image denoising

Abstract

The invention belongs to the technical field of image processing, and particularly relates to a low-dose CT image denoising recursion algorithm based on a residual encoding and decoding network. According to the specific technical scheme, the invention discloses a shallow residual error coding and decoding recursive network for low-dose CT image denoising. The recursive shallow residual encoding and decoding network reduces the complexity of the network by reducing the number of layers and the number of convolution kernels in the residual encoding and decoding network. The network performanceis improved by using a recursion process. According to the algorithm, end-to-end mapping is trained through a network to obtain a high-quality image. At each recursion, original low-dose CT images arecascaded to the next input. The problem of distortion of the image after multiple recursions can be effectively avoided, image features can be better extracted, detail information of the image can bereserved, the complexity of the network can be reduced, the network performance can be improved, image details of the denoised image can be well reserved, and the image structure is clearer.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a low-dose CT image denoising recursive algorithm based on a residual codec network. Background technique [0002] X-ray computed tomography (CT) provides the main anatomical and pathological information of the human body for medical diagnosis and treatment. However, the ionizing radiation generated during CT scanning will cause harm to the patient's body, and may even cause cancer, so the application of low-dose CT was born. A simple and easy way to reduce the radiation dose is to reduce the tube current, but this method can lead to a decrease in the signal-to-noise ratio of the projection data, making the CT image reconstructed by the filtered back projection (FBP) algorithm contain obvious bar artifacts. Shadow and noise affect the doctor's diagnosis. How to reconstruct high-quality CT images from raw noisy projection data has received extensive attentio...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the technical problem of complex network structure existing in the prior art, the present invention designs a shallow recursive network, which combines the constructed shallow residual Codec network recursion, use the same network structure to recursively construct a new network, reduce the complexity of the network by reducing the number of layers and the number of convolution kernels in the residual codec network, and use the recursive process to achieve the purpose of obtaining high-quality images

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