The data driven industry 4.0 and increasing mass-customization of additive manufacturing products require a flexible and high-throughput integration of a 100% quality inspection. While XCT has shown to be a viable non-destructive testing and measurement technique, the long acquisition times remain a stumbling block for a cost-efficient integration in production environments. To mitigate the reduced reconstruction quality from fast acquisition protocols a new sinogram interpolation algorithm is proposed. First, the method re-samples the acquired sinogram data to a new data representation which is called a spinogram. Second, the alternative data format is used to predict the missing X-ray projections using a deep learning network. We implemented this method for real XCT scans and validated on a dataset of five laser sintered objects. When applying the proposed method before the reconstruction step, less noise is present in the reconstructed and segmented volumes. This improves the feature analysis of the measured objects while preserving a lower acquisition time, hence extending the use of XCT towards fast quality inspections.

中文翻译：

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基于机器学习的 X 射线计算机断层扫描正弦图插值，经实验数据验证

数据驱动的工业 4.0 和增材制造产品日益大规模定制化需要灵活、高通量的 100% 质量检测集成。虽然 XCT 已被证明是一种可行的无损测试和测量技术，但较长的采集时间仍然是生产环境中经济高效集成的绊脚石。为了减轻快速采集协议导致的重建质量下降，提出了一种新的正弦图插值算法。首先，该方法将采集的正弦图数据重新采样为新的数据表示形式，称为脊柱图。其次，使用替代数据格式通过深度学习网络来预测丢失的 X 射线投影。我们在真实的 XCT 扫描中实现了这种方法，并在五个激光烧结物体的数据集上进行了验证。当在重建步骤之前应用所提出的方法时，重建和分割的体积中存在较少的噪声。这改进了被测物体的特征分析，同时保持较低的采集时间，从而将 XCT 的使用扩展到快速质量检查。