DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction.
DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction.
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DOI：
10.1109/tmi.2021.3078067
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发表时间：
2021-11
影响因子：
10.6
通讯作者：
Wang G
中科院分区：
文献类型：
--
作者：
Wu W;Hu D;Niu C;Yu H;Vardhanabhuti V;Wang G
深度学习在断层图像重建领域引起了越来越多的关注，特别是对于CT，MRI，PET/SPECT，超声和光学成像。在各种主题中，稀疏视图CT仍然是一个挑战，其目标是从超稀疏投影中重建出像样的图像。为了解决这一问题，本文提出了一种基于双域残差的优化网络（DRONE）。DRONE由三个模块组成，分别用于嵌入，细化和感知。在嵌入模块中，首先扩展稀疏正弦图。然后，稀疏视图伪影有效地抑制图像域网络。在此之后，细化模块集中在恢复图像细节的残留数据和图像域协同。最后，在感知模块中对数据和图像域中的嵌入和细化组件的结果进行正则化，以优化图像质量，从而确保测量结果与具有压缩感知核心感知的图像之间的一致性。DRONE网络在临床前和临床数据集上进行了训练、验证和测试，证明了其在边缘保留、特征恢复和重建精度方面的优势。
Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from ultra-sparse projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed by the image domain networks. After that, the refinement module focuses on the recovery of image details in the residual data and image domains synergistically. Finally, the results from embedding and refinement components in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy.