Abstract
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.
Abstract (translated)
锥束X射线计算机断层扫描（XCT）是一种重要的成像技术，用于生成内部结构的三维重建，在医学和工业成像等领域有广泛的应用。高质量的重建通常需要大量的X射线测量；这一过程可能缓慢且昂贵，尤其是在处理密度较高的材料时。最近的研究通过在插件即用（PnP）重构框架中引入减少伪影的先验方法，从稀疏视图XCT扫描中改善了图像质量，并提高了基于深度学习解决方案的泛化能力。然而，这种方法使用二维卷积神经网络（CNN）来减少伪影，只能捕获三维重建中的片层独立信息，从而限制了性能。 在这篇论文中，我们提出了一种PnP重构方法，该方法采用2.5D减伪影CNN作为先验。这种策略利用了相邻切片之间的相互关系，捕捉更丰富的空间上下文，同时保持计算效率。我们证明这种方法不仅提高了重建的质量，还使模型能够直接抑制常见的XCT伪影（如硬射线效应），从而消除了对预处理以纠正这些伪影的需求。 在实验和合成的锥束XCT数据上的实验证明了所提出的方法比使用二维先验更能保留精细结构细节，例如孔径大小和形状，这导致缺陷检测更为准确。特别是，我们展示了使用仅通过模拟扫描训练的2.5D减伪影先验模型，在实验性XCT数据上具有很强的表现力，突显了该方法跨领域泛化的潜力。
URL
https://arxiv.org/abs/2506.14719
