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。
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.
作者
乔一瑜
乔志伟
QIAO Yiyu;QIAO Zhiwei(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China)
出处
《CT理论与应用研究（中英文）》
2022年第6期697-707,I0002,共12页
Computerized Tomography Theory and Applications
基金
国家自然科学基金面上项目(模型与数据耦合驱动的快速四维EPRI肿瘤氧成像(62071281))
中央引导地方科技发展资金项目(新型TV和学习先验联合约束的快速四维EPRI成像方法(YDZJSX2021A003))
山西省重点研发计划(电子顺磁共振成像(EPRI)中美联合实验室平台建设(201803D421012))
山西省留学人员科技活动择优资助项目(基于压缩感知的四维EPRI成像方法研究(2018-172))
山西省回国留学人员科研资助项目(基于新型四维TV正则机理的快速EPRI肿瘤氧成像方法研究(2020-008))。
关键词
低剂量CT
自注意力机制
卷积神经网络
残差连接
low dose CT
self attention mechanism
convolutional neural network
residual connection
