A Bayesian approach for CT reconstruction with defect detection for subsea pipelines
A Bayesian approach for CT reconstruction with defect detection for subsea pipelines
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DOI：
10.1088/1361-6420/ad1348
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发表时间：
2024-02-01
期刊：
影响因子：
2.1
通讯作者：
Jorgensen，Jakob S.
中科院分区：
文献类型：
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作者：
Christensen，Silja L.;Riis，Nicolai A. B.;Jorgensen，Jakob S.
海底管道可以通过2D横截面X射线计算机断层扫描（CT）进行检查。传统的重建方法产生管道内部的图像，可以对该图像进行后处理以检测可能的缺陷。在本文中，我们提出了一种新的贝叶斯CT重建方法与内置的缺陷检测。我们将重建分解成两个图像的总和;一个包含整体管道结构，一个包含缺陷，并同时在吉布斯方案中推断图像。我们的方法要求两个图像的先验信息是非常明显的，即第一个图像应该包含大规模和分层的管道结构，第二个图像应该包含小的，连贯的缺陷。我们证明了我们的方法与数值实验使用合成和真实的CT数据扫描的海底管道的情况下，充分和有限的数据。实验表明，该方法在各种数据设置的有效性，重建质量与现有技术相媲美，同时还提供了缺陷检测与不确定性量化。
Subsea pipelines can be inspected via 2D cross-sectional x-ray computed tomography (CT). Traditional reconstruction methods produce an image of the pipe's interior that can be post-processed for detection of possible defects. In this paper we propose a novel Bayesian CT reconstruction method with built-in defect detection. We decompose the reconstruction into a sum of two images; one containing the overall pipe structure, and one containing defects, and infer the images simultaneously in a Gibbs scheme. Our method requires that prior information about the two images is very distinct, ie the first image should contain the large-scale and layered pipe structure, and the second image should contain small, coherent defects. We demonstrate our methodology with numerical experiments using synthetic and real CT data from scans of subsea pipes in cases with full and limited data. Experiments demonstrate the effectiveness of the proposed method in various data settings, with reconstruction quality comparable to existing techniques, while also providing defect detection with uncertainty quantification.