PDF(Pubmed)
Abstract：
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.
摘要：
X线计算机断层扫描（CT）成像技术已成为临床检查中必不可少的诊断工具。然而,它会带来电离辐射的风险，降低辐射剂量是当前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,近年来取得了重大进展。特别是,基于深度学习的稀疏视图重建方法取得了良好的效果。然而，在超稀疏条件下有效地恢复图像细节仍然是一个挑战。为了应对这一挑战，本文提出了一种高频增强和注意力引导的学习网络(HEAL)。HEAL包括三种优化策略来实现细节增强：首先，我们引入了一个双域渐进增强模块，，它利用每个域内的保真度约束和跨域的一致性约束来有效地缩小解决方案空间。其次,我们结合了通道和空间注意力机制来改善网络的功能扩展过程。最后,我们提出了一个高频分量增强正则化项，它将残差学习与方向加权总变异相结合，利用方向线索来有效区分噪声和纹理。HEAL网络经过训练，在60个视图和30个视图的不同超稀疏配置下进行了验证和测试，展示其在重建精度和细节增强方面的优势。
