ISSN 1004-4140
CN 11-3017/P
乔一瑜, 乔志伟. 基于CNN和Transformer耦合网络的低剂量CT图像重建方法[J]. CT理论与应用研究, 2022, 31(6): 697-707. DOI: 10.15953/j.ctta.2022.114.
引用本文: 乔一瑜, 乔志伟. 基于CNN和Transformer耦合网络的低剂量CT图像重建方法[J]. CT理论与应用研究, 2022, 31(6): 697-707. DOI: 10.15953/j.ctta.2022.114.
QIAO Y Y, QIAO Z W. Low-dose CT image reconstruction method based on CNN and transformer coupling network[J]. CT Theory and Applications, 2022, 31(6): 697-707. DOI: 10.15953/j.ctta.2022.114. (in Chinese).
Citation: QIAO Y Y, QIAO Z W. Low-dose CT image reconstruction method based on CNN and transformer coupling network[J]. CT Theory and Applications, 2022, 31(6): 697-707. DOI: 10.15953/j.ctta.2022.114. (in Chinese).

基于CNN和Transformer耦合网络的低剂量CT图像重建方法

Low-dose CT Image Reconstruction Method Based on CNN and Transformer Coupling Network

  • 摘要: 在投影角度个数不变的情况下,降低每个角度下的射线剂量,是一种有效的低剂量CT实现方式,然而,这会使得重建图像的噪声较大。当前,以卷积神经网络(CNN)为代表的深度学习图像去噪方法已经成为低剂量CT图像去噪的经典方法。受Transformer在计算机视觉任务中展现的良好性能的启发,本文提出一种CNN和Transformer耦合的网络(CTC),以进一步提高CT图像去噪的性能。CTC网络综合运用CNN的局部信息关联能力和Transformer的全局信息捕捉能力,构建8个由CNN部件和一种改进的Transformer部件构成的核心网络块,并基于残差连接机制和信息复用机制将之互联。与现有4种去噪网络比较,CTC网络去噪能力更强,可以实现高精度低剂量CT图像重建。

     

    Abstract: Under the condition that the number of projection angles is constant, reducing the radiation dose under each angle is an effective way to realize low-dose CT. However, the reconstructed images obtained through this method can be very noisy. At present, the deep learning image denoising method represented by convolutional neural networks (CNN) has become a classical method for low-dose CT image denoising. Inspired by the good performance of transformer in computer vision tasks, this paper proposes a CNN transformer coupling network (CTC) to further improve the performance of CT image denoising. CTC network makes comprehensive use of local information association ability of CNN and global information capture ability of transformer, constructs eight core network blocks composed of CNN components and an improved transformer component, which are interconnected based on residual connection mechanism and information reuse mechanism. Compared with the existing four denoising networks, CTC network demonstrate better denoising ability and can realize high-precision low-dose CT image reconstruction.

     

/

返回文章
返回