An adaptive self-guided wavelet convolutional neural network with compound loss for low-dose CT denoising

Abstract

Computed Tomography (CT) is an imaging method widely used in clinical, industrial, and other applications. Furthermore, it is one of the common methods of modern clinical medical imaging diagnosis. However, excessive radiation doses in CT scans can cause harm to the human body. Reducing the radiation dose will cause serious degradation of the reconstructed image quality, produce speckle noise and streak artifacts, and affect the accuracy of clinical medical diagnosis. In order to obtain high-quality images while reducing CT radiation dose, we propose an adaptive self-guided wavelet convolutional neural network (ASWCNN) to convert low-dose CT (LDCT) images into normal-dose CT (NDCT) images as true as possible. In our ASWCNN, combining wavelet transform and sub-pixel convolution, a top-down self-guiding structure is proposed as the overall architecture of the network. An adjustable pyramid residual block (APRB) is proposed to self-adaption extract multi-scale and diversity information features as image resolution decreases along with the network. The adjacent scale information fusion block (ASIFB) is proposed to fuse the information features between adjacent scales step by step to improve the stability of network training. At the same time, we use the cross-latitude mixed attention block (CMAB) as the feature enhancement block of our network to enhance the fused feature information, enhance the effective information and suppress the useless information. In addition, we proposed the correction reconstruction block (CRB) to reduce the gap between the obtained denoised CT image and the given NDCT image. Finally, we take the compound loss combining the pixel-level loss, structure-perceived loss, and gradient loss as our loss function. Experimental results show that our proposed method can effectively retain the structure and texture information of CT images while removing noise and artifacts. At the same time, compared with other methods, better subjective visual evaluation and objective quantitative evaluation are obtained, and the network generalization ability is good.