用深度学习快速重建复杂金属部件的工业CT图像。
A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography
- 用训练好的CNN做先验，自动调节参数，加速迭代重建。
- 仅几轮迭代就还原出高精度3D图像，连厚实金属件也能处理。
- 对不同扫描条件数据泛化好，抗噪抗伪影，适合工业场景。
大探测器锥束X射线计算机断层扫描（XCT）在微米级材料与零件表征中至关重要。本文提出一种基于深度神经网络的新型迭代重建算法，将经过伪影抑制训练的CNN作为先验模型，并实现自动正则化参数选择，专为大规模工业锥束XCT数据设计。该方法在仅数次迭代内即可实现高质量3D重建，尤其适用于传统CT难以成像的致密厚金属部件。此外，该方法在不同扫描条件下获取的分布外数据上也表现出良好泛化能力。其有效抑制了显著噪声和条纹伪影，在相同数据集上训练的监督学习方法中表现更优。
原文摘要 · Abstract (English)
Cone-beam X-ray Computed Tomography (XCT) with large detectors and corresponding large-scale 3D reconstruction plays a pivotal role in micron-scale characterization of materials and parts across various industries. In this work, we present a novel deep neural network-based iterative algorithm that integrates an artifact reduction-trained CNN as a prior model with automated regularization parameter selection, tailored for large-scale industrial cone-beam XCT data. Our method achieves high-quality 3D reconstructions even for extremely dense thick metal parts - which traditionally pose challenges to industrial CT images - in just a few iterations. Furthermore, we show the generalizability of our approach to out-of-distribution scans obtained under diverse scanning conditions. Our method effectively handles significant noise and streak artifacts, surpassing state-of-the-art supervised learning methods trained on the same data.
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