A long-term goal in CT imaging is to achieve fast and accurate 3D reconstruction from sparse-view projections, thereby reducing radiation exposure, lowering system cost, and enabling timely imaging in clinical workflows. Recent feed-forward approaches have shown strong potential toward this overarching goal, yet their results still suffer from artifacts and loss of fine details. In this work, we introduce Iterative Latent Volumes (ILV), a feed-forward framework that integrates data-driven priors with classical iterative reconstruction principles to overcome key limitations of prior feed-forward models in sparse-view CBCT reconstruction. At its core, ILV constructs an explicit 3D latent volume that is repeatedly updated by conditioning on multi-view X-ray features and the learned anatomical prior, enabling the recovery of fine structural details beyond the reach of prior feed-forward models. In addition, we develop and incorporate several key architectural components, including an X-ray feature volume, group cross-attention, efficient self-attention, and view-wise feature aggregation, that efficiently realize its core latent volume refinement concept. Extensive experiments on a large-scale dataset of approximately 14,000 CT volumes demonstrate that ILV significantly outperforms existing feed-forward and optimization-based methods in both reconstruction quality and speed. These results show that ILV enables fast and accurate sparse-view CBCT reconstruction suitable for clinical use. The project page is available at: https://sngryonglee.github.io/ILV/.

翻译：CT成像领域的一个长期目标是实现基于稀疏视角投影的快速精确三维重建，从而降低辐射剂量、减少系统成本，并满足临床工作流程中的即时成像需求。近年来，前馈式方法在这一总体目标上展现出巨大潜力，但其重建结果仍存在伪影与细节丢失问题。本研究提出迭代潜在体（ILV），一种将数据驱动先验与经典迭代重建原理相结合的前馈框架，旨在克服现有前馈模型在稀疏视角锥束CT重建中的关键局限。ILV的核心在于构建一个显式的三维潜在体，该潜在体通过多视角X射线特征与学习到的解剖先验进行条件化更新，从而恢复以往前馈模型难以捕捉的精细结构细节。此外，我们设计并整合了多项关键架构组件，包括X射线特征体、分组交叉注意力、高效自注意力及视角特征聚合机制，以高效实现其核心的潜在体优化理念。在大规模数据集（约14,000例CT体数据）上的广泛实验表明，ILV在重建质量与速度上均显著优于现有的前馈式及基于优化的方法。这些结果证明ILV能够实现适用于临床的快速精确稀疏视角锥束CT重建。项目页面详见：https://sngryonglee.github.io/ILV/。