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HOICNet: Low-Dose CT image denoising network based on higher-order feature attention mechanism and irregular convolution
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DOI：10.1016/j.image.2025.117457.png)
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Abstract
En 中文
Convolution Neural Networks (CNNs) with attention mechanisms show great potential for improving low-dose computed tomography (LDCT) image quality. However, most of these methods use first-order statistics for channel or space processing, ignoring the higher-order statistics of the channel or space features. In addition, the conventional convolution has limited receptive field and a poor performance on the edge of LDCT images. In this study, we aim to develop a CNN model incorporating higher-order feature attention mechanism that both enlarges the receptive field and clearly recovers edges and details. We propose an LDCT image denoising network named as HOICNet based on a higher-order feature attention mechanism and irregular convolution. Specifically, we first propose a new higher-order feature attention mechanism that utilizes higher-order feature statistics to enhance features in different channels and spatial regions. Second, we propose a new irregular convolutional feature extraction module (ICFE) that contains self-calibrating convolution (SC) and side window convolution (SWC). SC is used to enlarge receptive fields, and SWC is used to improve the edge information in denoised images. Finally, we introduce the contrast regularization mechanism (CRM) with positive and negative samples to bring the denoised image closer and closer to the positive samples while moving away from the negative samples to alleviate the problem of over-smoothing of the denoised images. Our experimental results show that the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM), the root mean square error (RMSE) and the visual information fidelity (VIF) values achieved significant improvements in both the AAPM dataset and the piglet dataset.
Keywords:
Convolution neural network
Higher-order feature attention mechanism
Irregular convolutional feature extraction module
Contrast regularization mechanism
Low-dose CT image denoising
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