Zhang Ju, Ye Lieli, Gong Weiwei, Chen Mingyang, Liu Guangyu, Cheng Yun
College of Information Science and Technology, Hangzhou Normal University, Hangzhou, 310030, China.
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China.
J Imaging Inform Med. 2025 Apr;38(2):1245-1264. doi: 10.1007/s10278-024-01254-z. Epub 2024 Sep 11.
Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch and multi-scale residual attention-based network for low-dose CT image denoising is proposed. It adopts a two-branch framework structure, to extract and fuse image features at shallow and deep levels respectively, to recover image texture and structure information as much as possible. We propose the adaptive dynamic convolution block (ADCB) in the local information extraction layer. It can effectively extract the detailed information of low-dose CT denoising and enables the network to better capture the local details and texture features of the image, thereby improving the denoising effect and image quality. Multi-scale edge enhancement attention block (MEAB) is proposed in the global information extraction layer, to perform feature fusion through dilated convolution and a multi-dimensional attention mechanism. A multi-scale residual convolution block (MRCB) is proposed to integrate feature information and improve the robustness and generalization of the network. To demonstrate the effectiveness of our method, extensive comparison experiments are conducted and the performances evaluated on two publicly available datasets. Our model achieves 29.3004 PSNR, 0.8659 SSIM, and 14.0284 RMSE on the AAPM-Mayo dataset. It is evaluated by adding four different noise levels σ = 15, 30, 45, and 60 on the Qin_LUNG_CT dataset and achieves the best results. Ablation studies show that the proposed ADCB, MEAB, and MRCB modules improve the denoising performances significantly. The source code is available at https://github.com/Ye111-cmd/LDMANet .
近年来，基于深度学习的低剂量医学CT图像去噪受到了学术研究人员和医生的广泛关注，并在临床实践中显示出重要的应用价值。在这项工作中，提出了一种新颖的基于双分支和多尺度残差注意力的低剂量CT图像去噪网络。它采用双分支框架结构，分别在浅层和深层提取并融合图像特征，以尽可能恢复图像纹理和结构信息。我们在局部信息提取层中提出了自适应动态卷积块（ADCB）。它可以有效地提取低剂量CT去噪的详细信息，使网络能够更好地捕捉图像的局部细节和纹理特征，从而提高去噪效果和图像质量。在全局信息提取层中提出了多尺度边缘增强注意力块（MEAB），通过空洞卷积和多维度注意力机制进行特征融合。提出了多尺度残差卷积块（MRCB）以整合特征信息并提高网络的鲁棒性和泛化能力。为了证明我们方法的有效性，进行了广泛的对比实验，并在两个公开可用的数据集上评估了性能。我们的模型在AAPM-Mayo数据集上实现了29.3004的PSNR、0.8659的SSIM和14.0284的RMSE。在Qin_LUNG_CT数据集上通过添加四个不同的噪声水平σ = 15、30、45和60进行评估，并取得了最佳结果。消融研究表明，所提出的ADCB、MEAB和MRCB模块显著提高了去噪性能。源代码可在https://github.com/Ye111-cmd/LDMANet获取。
