标题： 组织引导神经断层扫描的强度场分解 显示英文标题

标题： Intensity Field Decomposition for Tissue-Guided Neural Tomography

摘要： 锥束计算机断层扫描（CBCT）通常需要数百个X射线投影，这引发了人们对辐射暴露的担忧。虽然稀疏视图重建通过使用较少的投影减少了曝光，但它难以实现令人满意的图像质量。为了解决这一挑战，本文介绍了一种新颖的稀疏视图CBCT重建方法，该方法赋予神经场以人体组织正则化。我们的方法称为组织引导的神经断层扫描（TNT），其灵感来源于CBCT中骨与软组织之间的强度差异。直观上，分离这些成分可能有助于神经场的学习过程。更具体地说，TNT由一个异构四重网络及其对应的训练策略组成。该网络将强度场表示为软组织和硬组织成分及其相应纹理的组合。我们通过估计组织投影的指导来训练网络，从而高效学习网络头部所需的模式。广泛的实验表明，所提出的方法显著改善了稀疏视图CBCT重建，在投影数量从10到60的有限范围内。与基于最先进的神经渲染的方法相比，我们的方法在更少的投影和更快的收敛速度下实现了可比的重建质量。 显示英文摘要

摘要： Cone-beam computed tomography (CBCT) typically requires hundreds of X-ray projections, which raises concerns about radiation exposure. While sparse-view reconstruction reduces the exposure by using fewer projections, it struggles to achieve satisfactory image quality. To address this challenge, this article introduces a novel sparse-view CBCT reconstruction method, which empowers the neural field with human tissue regularization. Our approach, termed tissue-guided neural tomography (TNT), is motivated by the distinct intensity differences between bone and soft tissue in CBCT. Intuitively, separating these components may aid the learning process of the neural field. More precisely, TNT comprises a heterogeneous quadruple network and the corresponding training strategy. The network represents the intensity field as a combination of soft and hard tissue components, along with their respective textures. We train the network with guidance from estimated tissue projections, enabling efficient learning of the desired patterns for the network heads. Extensive experiments demonstrate that the proposed method significantly improves the sparse-view CBCT reconstruction with a limited number of projections ranging from 10 to 60. Our method achieves comparable reconstruction quality with fewer projections and faster convergence compared to state-of-the-art neural rendering based methods.