School of Information and Communication Engineering, North University of China, Taiyuan Shanxi Province, China.
State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China.
J Xray Sci Technol. 2023;31(4):757-775. doi: 10.3233/XST-230020.
In view of the underlying health risks posed by X-ray radiation, the main goal of the present research is to achieve high-quality CT images at the same time as reducing x-ray radiation. In recent years, convolutional neural network (CNN) has shown excellent performance in removing low-dose CT noise. However, previous work mainly focused on deepening and feature extraction work on CNN without considering fusion of features from frequency domain and image domain.
To address this issue, we propose to develop and test a new LDCT image denoising method based on a dual-domain fusion deep convolutional neural network (DFCNN).
This method deals with two domains, namely, the DCT domain and the image domain. In the DCT domain, we design a new residual CBAM network to enhance the internal and external relations of different channels while reducing noise to promote richer image structure information. For the image domain, we propose a top-down multi-scale codec network as a denoising network to obtain more acceptable edges and textures while obtaining multi-scale information. Then, the feature images of the two domains are fused by a combination network.
The proposed method was validated on the Mayo dataset and the Piglet dataset. The denoising algorithm is optimal in both subjective and objective evaluation indexes as compared to other state-of-the-art methods reported in previous studies.
The study results demonstrate that by applying the new fusion model denoising, denoising results in both image domain and DCT domain are better than other models developed using features extracted in the single image domain.
鉴于 X 射线辐射带来的潜在健康风险，本研究的主要目标是在降低 X 射线辐射的同时获得高质量的 CT 图像。近年来，卷积神经网络（CNN）在去除低剂量 CT 噪声方面表现出了优异的性能。然而，以前的工作主要集中在对 CNN 的深化和特征提取工作上，而没有考虑到来自频域和图像域的特征融合。
为了解决这个问题，我们提出开发和测试一种新的基于双域融合深度卷积神经网络（DFCNN）的低剂量 CT 图像去噪方法。
该方法处理两个域，即 DCT 域和图像域。在 DCT 域中，我们设计了一个新的残差 CBAM 网络，以增强不同通道之间的内部和外部关系，同时减少噪声，促进更丰富的图像结构信息。对于图像域，我们提出了一个自顶向下的多尺度编解码器网络作为去噪网络，在获得多尺度信息的同时获得更可接受的边缘和纹理。然后，通过组合网络融合两个域的特征图像。
该方法在 Mayo 数据集和 Piglet 数据集上进行了验证。与以前研究中报道的其他最先进方法相比，该去噪算法在主观和客观评价指标上都是最优的。
研究结果表明，通过应用新的融合模型去噪，与仅使用单一图像域提取的特征开发的其他模型相比，图像域和 DCT 域的去噪效果都更好。
