Li Guang, Deng Zhenhao, Ge Yongshuai, Luo Shouhua
Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Bioengineering (Basel). 2024 Jun 25;11(7):646. doi: 10.3390/bioengineering11070646.
X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of the main methods for reducing radiation dose, has made significant progress in recent years. In particular, sparse-view reconstruction methods based on deep learning have shown promising results. Nevertheless, efficiently recovering image details under ultra-sparse conditions remains a challenge. To address this challenge, this paper proposes a high-frequency enhanced and attention-guided learning Network (HEAL). HEAL includes three optimization strategies to achieve detail enhancement: Firstly, we introduce a dual-domain progressive enhancement module, which leverages fidelity constraints within each domain and consistency constraints across domains to effectively narrow the solution space. Secondly, we incorporate both channel and spatial attention mechanisms to improve the network's feature-scaling process. Finally, we propose a high-frequency component enhancement regularization term that integrates residual learning with direction-weighted total variation, utilizing directional cues to effectively distinguish between noise and textures. The HEAL network is trained, validated and tested under different ultra-sparse configurations of 60 views and 30 views, demonstrating its advantages in reconstruction accuracy and detail enhancement.
X射线计算机断层扫描（CT）成像技术已成为临床检查中不可或缺的诊断工具。然而，它存在电离辐射风险，这使得降低辐射剂量成为当前CT成像研究的热点之一。稀疏视图成像作为降低辐射剂量的主要方法之一，近年来取得了显著进展。特别是，基于深度学习的稀疏视图重建方法已显示出有前景的结果。然而，在超稀疏条件下有效恢复图像细节仍然是一个挑战。为应对这一挑战，本文提出了一种高频增强和注意力引导学习网络（HEAL）。HEAL包括三种实现细节增强的优化策略：首先，我们引入了一个双域渐进增强模块，该模块利用每个域内的保真度约束和跨域的一致性约束来有效缩小解空间。其次，我们结合了通道和空间注意力机制，以改善网络的特征缩放过程。最后，我们提出了一个高频分量增强正则化项，将残差学习与方向加权全变差相结合，利用方向线索有效区分噪声和纹理。HEAL网络在60视图和30视图的不同超稀疏配置下进行了训练、验证和测试，证明了其在重建精度和细节增强方面的优势。
