Adaptive edge prior-based deep attention residual network for low-dose CT image denoising

Abstract

Improving the diagnostic quality of low-dose CT (LDCT) images relies on effective noise removal. Recent advancements have highlighted the widespread use of deep residual networks for LDCT image denoising. These networks possess properties that aid in preserving image integrity and optimizing model performance. However, the denoising process faces challenges due to the complex patterns and intensity similarities between edge details and lesion regions. To address this issue, this paper introduces a novel approach called the cross-scale attentional residual network (RCANet), which utilizes an adaptive edge prior for LDCT image denoising. The adaptive edge prior enhances the denoising network’s ability to retain image boundary features and uniqueness. To distinguish subtle differences between LDCT image edge details and lesion areas, a cross-scale mapping dual-element module (CMDM) is designed to preserve rich edge texture information during model training. To prevent over-smoothing of denoised results, a compound loss function is proposed, combining MSE loss and multi-scale attention residual perception loss. To validate the effectiveness of the method, experiments were conducted on the AAPM-Mayo Clinic LDCT Grand Challenge dataset. The results demonstrate that RCANet surpasses state-of-the-art residual structure-based network models and performs comparably to leading denoising algorithms.