Zhang Yikun, Hu Dianlin, Yan Zhihong, Zhao Qingxian, Quan Guotao, Luo Shouhua, Zhang Yi, Chen Yang
Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing, Jiangsu, China.
School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.
Med Image Anal. 2023 Jan;83:102650. doi: 10.1016/j.media.2022.102650. Epub 2022 Oct 17.
Dual-energy cone-beam computed tomography (DE-CBCT) is a promising imaging technique with foreseeable clinical applications. DE-CBCT images acquired with two different spectra can provide material-specific information. Meanwhile, the anatomical consistency and energy-domain correlation result in significant information redundancy, which could be exploited to improve image quality. In this context, this paper develops the Transformer-Integrated Multi-Encoder Network (TIME-Net) for DE-CBCT to remove the limited-angle artifacts. TIME-Net comprises three encoders (image encoder, prior encoder, and transformer encoder), two decoders (low- and high-energy decoders), and one feature fusion module. Three encoders extract various features for image restoration. The feature fusion module compresses these features into more compact shared features and feeds them to the decoders. Two decoders perform differential learning for DE-CBCT images. By design, TIME-Net could obtain high-quality DE-CBCT images using two complementary quarter-scans, holding great potential to reduce radiation dose and shorten the acquisition time. Qualitative and quantitative analyses based on simulated data and real rat data have demonstrated the promising performance of TIME-Net in artifact removal, subtle structure restoration, and reconstruction accuracy preservation. Two clinical applications, virtual non-contrast (VNC) imaging and iodine quantification, have proved the potential utility of the DE-CBCT images provided by TIME-Net.
双能锥束计算机断层扫描（DE-CBCT）是一种具有可预见临床应用前景的成像技术。用两种不同光谱采集的DE-CBCT图像可以提供物质特异性信息。同时，解剖学一致性和能量域相关性导致了显著的信息冗余，可利用这些冗余来提高图像质量。在此背景下，本文开发了用于DE-CBCT的Transformer集成多编码器网络（TIME-Net）以去除有限角度伪影。TIME-Net由三个编码器（图像编码器、先验编码器和Transformer编码器）、两个解码器（低能和解码器和高能解码器）和一个特征融合模块组成。三个编码器提取用于图像恢复的各种特征。特征融合模块将这些特征压缩成更紧凑的共享特征，并将其馈送到解码器。两个解码器对DE-CBCT图像进行差分学习。通过设计，TIME-Net可以使用两个互补的四分之一扫描获得高质量的DE-CBCT图像，在降低辐射剂量和缩短采集时间方面具有巨大潜力。基于模拟数据和真实大鼠数据的定性和定量分析表明，TIME-Net在去除伪影、恢复细微结构和保持重建精度方面具有良好的性能。虚拟非增强（VNC）成像和碘定量这两种临床应用证明了TIME-Net提供的DE-CBCT图像的潜在实用性。
