Cone-beam X-ray computed tomography (XCT) is an essential imaging technique for generating 3D reconstructions of internal structures, with applications ranging from medical to industrial imaging. Producing high-quality reconstructions typically requires many X-ray measurements; this process can be slow and expensive, especially for dense materials. Recent work incorporating artifact reduction priors within a plug-and-play (PnP) reconstruction framework has shown promising results in improving image quality from sparse-view XCT scans while enhancing the generalizability of deep learning-based solutions. However, this method uses a 2D convolutional neural network (CNN) for artifact reduction, which captures only slice-independent information from the 3D reconstruction, limiting performance. In this paper, we propose a PnP reconstruction method that uses a 2.5D artifact reduction CNN as the prior. This approach leverages inter-slice information from adjacent slices, capturing richer spatial context while remaining computationally efficient. We show that this 2.5D prior not only improves the quality of reconstructions but also enables the model to directly suppress commonly occurring XCT artifacts (such as beam hardening), eliminating the need for artifact correction pre-processing. Experiments on both experimental and synthetic cone-beam XCT data demonstrate that the proposed method better preserves fine structural details, such as pore size and shape, leading to more accurate defect detection compared to 2D priors. In particular, we demonstrate strong performance on experimental XCT data using a 2.5D artifact reduction prior trained entirely on simulated scans, highlighting the proposed method's ability to generalize across domains.

翻译：锥束X射线计算机断层扫描（XCT）是一种生成内部结构三维重建的关键成像技术，其应用涵盖从医学到工业成像的广泛领域。生成高质量重建通常需要大量X射线测量，这一过程可能耗时且昂贵，尤其对于高密度材料而言。近期研究将伪影抑制先验融入即插即用（PnP）重建框架，在提升稀疏视图XCT扫描图像质量的同时，增强了基于深度学习的解决方案的泛化能力，展现出良好前景。然而，该方法使用二维卷积神经网络（CNN）进行伪影抑制，仅能捕获三维重建中切片间独立的信息，限制了其性能。本文提出一种采用2.5D伪影抑制CNN作为先验的PnP重建方法。该方法利用相邻切片间的层间信息，在保持计算效率的同时捕获更丰富的空间上下文。我们证明，这种2.5D先验不仅提升了重建质量，还能直接抑制XCT中常见的伪影（如射束硬化），从而无需进行伪影校正预处理。在实验与合成锥束XCT数据上的测试表明，相较于二维先验，所提方法能更好地保留孔隙尺寸与形状等细微结构特征，从而实现更精确的缺陷检测。特别值得注意的是，我们使用完全基于模拟扫描训练的2.5D伪影抑制先验，在实验XCT数据上展现出优异性能，这凸显了该方法跨领域泛化的能力。