Zhu Jiongtao, Su Ting, Zhang Xin, Yang Jiecheng, Mi Donghua, Zhang Yunxin, Gao Xiang, Zheng Hairong, Liang Dong, Ge Yongshuai
Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China.
Department of Vascular Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, People's Republic of China.
Phys Med Biol. 2022 Jul 12;67(14). doi: 10.1088/1361-6560/ac7b09.
In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstructing three different material distribution volumes from the dual-energy CBCT projection data.In Triple-CBCT, the features of the sinogram and the CT image are independently extracted and cascaded via a customized domain transform network module. This Triple-CBCT network was trained by numerically synthesized dual-energy CBCT data, and was tested with experimental dual-energy CBCT data of the Iodine-CaClsolution and pig leg specimen scanned on an in-house benchtop system.Results show that the information stored in both the sinogram and CT image domains can be used together to improve the decomposition quality of multiple materials (water, iodine, CaClor bone) from the dual-energy projections. In addition, both the numerical and experimental results demonstrate that the Triple-CBCT is able to generate high-fidelity dual-energy CBCT basis images.An innovative end-to-end network that joints the sinogram and CT image domain information is developed to facilitate high quality automatic decomposition from the dual-energy CBCT scans.
在这项工作中，提出了一种名为Triple-CBCT的专用端到端深度卷积神经网络，以证明从双能CBCT投影数据重建三种不同材料分布体积的可行性。在Triple-CBCT中，通过定制的域变换网络模块独立提取并级联正弦图和CT图像的特征。该Triple-CBCT网络由数值合成的双能CBCT数据训练，并使用在内部台式系统上扫描的碘-氯化钙溶液和猪腿标本的实验双能CBCT数据进行测试。结果表明，正弦图和CT图像域中存储的信息可以一起用于提高双能投影中多种材料（水、碘、氯化钙或骨骼）的分解质量。此外，数值和实验结果均表明，Triple-CBCT能够生成高保真双能CBCT基图像。开发了一种连接正弦图和CT图像域信息的创新端到端网络，以促进双能CBCT扫描的高质量自动分解。
