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使用多能量X射线透射技术对叠加状态下的铜、铁和石材厚度估算的可行性研究
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Abstract:
自然界中，铜和铁元素常以伴生状态存在于矿石中。为了估算铜铁伴生矿中的元素含量，准确估算铜、铁和石材在叠加状态下的厚度至关重要。本文提出了一种基于多能量X射线透射(ME-XRT)原理的厚度估算方法，利用光子计数探测器(PCD)采集多能量数据，并结合机器学习算法进行系数修正。该方法包括多能量区间的X射线衰减图像获取、图像预处理、特征提取以及基于机器学习的系数修正。实验结果表明，该方法能够有效估算三种材料叠加状态下的厚度分布。
In nature, copper and iron elements often coexist in ores. To estimate the elemental content in copper-iron associated ores, accurately determining the thickness of copper, iron, and stone in an overlapping state is crucial. This paper proposes a thickness estimation method based on the principle of Multi-Energy X-ray Transmission (ME-XRT), which utilizes a Photon Counting Detector (PCD) to acquire multi-energy data and combines machine learning algorithms for coefficient correction. The method includes the acquisition of X-ray attenuation images in multiple energy ranges, image preprocessing, feature extraction, and coefficient correction based on machine learning. Experimental results show that the proposed method can effectively estimate the thickness distribution of the three materials in an overlapping state.
| [1] | Craddock, P.T. and Meeks, N.D. (1987) Iron in Ancient Copper. Archaeometry, 29, 187-204. https://doi.org/10.1111/j.1475-4754.1987.tb00411.x |
| [2] | Robben, C. and Wotruba, H. (2019) Sensor-Based Ore Sorting Technology in Mining—Past, Present and Future. Minerals, 9, Article 523. https://doi.org/10.3390/min9090523 |
| [3] | Schena, G., Chiaruttini, C., Dreossi, D., Olivo, A. and Pani, S. (2002) Grade of Fine Composite Mineral Particles by Dual-Energy X-Ray Radiography. International Journal of Mineral Processing, 67, 101-122. https://doi.org/10.1016/s0301-7516(02)00037-6 |
| [4] | Scott, P.D., Krastev, E. and Nordson Dage, P.E. (2016) 2D X-Ray Inspection with Materials and Thickness Identification. Surface Mounting Technology, 32, 70-79. |
| [5] | Yeyu, Y., Wenbao, J., Daqian, H., Aiyun, S., Can, C., Mengcheng, Q., et al. (2021) Feasibility Study of a Method for Identification and Classification of Magnesium and Aluminum with ME-XRT. Journal of Instrumentation, 16, P11041. https://doi.org/10.1088/1748-0221/16/11/p11041 |
| [6] | Sim, L.M., Tan, T.C., Mon, A.A. and Wong, B.S. (2009) X-Ray Absorption-Based Technique to Measure the Thickness of Multi-Layered Structures. NDT & E International, 42, 291-296. https://doi.org/10.1016/j.ndteint.2008.12.002 |
| [7] | Schumacher, D., Zscherpel, U. and Ewert, U. (2016) Photon Counting and Energy Discriminating X-Ray Detectors—Benefits and Applications. 19th World Conference on Non-Destructive Testing (WCNDT 2016), Munich, 13-17 June 2016, 1-10. |
| [8] | Nakamura, Y., Higaki, T., Kondo, S., Kawashita, I., Takahashi, I. and Awai, K. (2022) An Introduction to Photon-Counting Detector CT (PCD CT) for Radiologists. Japanese Journal of Radiology, 41, 266-282. https://doi.org/10.1007/s11604-022-01350-6 |
| [9] | Takezawa, T., Uemoto, M. and Itoh, K. (2014) Combination of X-Ray Transmission and Eddy-Current Testing for the Closed-Loop Recycling of Aluminum Alloys. Journal of Material Cycles and Waste Management, 17, 84-90. https://doi.org/10.1007/s10163-013-0228-4 |
| [10] | Hubbell, J. H. (1969) Photon Cross Sections, Attenuation Coefficients and Energy Absorption Coefficients. National Bureau of Standards Report NSRDS-NBS29, Washington DC. |
| [11] | Rajendran, K., Walsh, M.F., de Ruiter, N.J.A., Chernoglazov, A.I., Panta, R.K., Butler, A.P.H., et al. (2014) Reducing Beam Hardening Effects and Metal Artefacts in Spectral CT Using Medipix3RX. Journal of Instrumentation, 9, P03015. https://doi.org/10.1088/1748-0221/9/03/p03015 |
| [12] | Rebuffel, V., Tartare, M., Brambilla, A., Moulin, V. and Verger, L. (2014) Multi-Energy X-Ray Techniques for NDT: A New Challenge. 11th European Conference on Non-Destructive Testing (ECNDT 2014), Prague, 6-10 October 2014, 1-10. |
| [13] | Wu, X., He, P., Long, Z., Guo, X., Chen, M., Ren, X., et al. (2019) Multi-Material Decomposition of Spectral CT Images via Fully Convolutional DenseNets. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 27, 461-471. https://doi.org/10.3233/xst-190500 |
| [14] | Clark, D.P., Holbrook, M. and Badea, C.T. (2018) Multi-Energy CT Decomposition Using Convolutional Neural Networks. Medical Imaging 2018: Physics of Medical Imaging, Houston, 10-15 February 2018, 415-423. https://doi.org/10.1117/12.2293728 |
| [15] | Abascal, J.F.P.J., Ducros, N., Pronina, V., Rit, S., Rodesch, P., Broussaud, T., et al. (2021) Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach. IEEE Access, 9, 25632-25647. https://doi.org/10.1109/access.2021.3056150 |
