Metal Artifact Reduction Algorithm for CT Images of Rock and Mineral Samples Based on Dual-domain Adaptive Network

ZUO Shunji; FENG Peng; HUANG Pan; YAN Shenghao; HE Peng; WEI Biao

doi:10.15953/j.ctta.2022.041

Volume 31 Issue 6

Nov. 2022

Turn off MathJax

Article Contents

Abstract

References

CT Theory and Applications > 2022 > 31(6): 783-792. > DOI: 10.15953/j.ctta.2022.041

ZUO S J, FENG P, HUANG P, et al. Metal artifact reduction algorithm for CT images of rock and mineral samples based on dual-domain adaptive network[J]. CT Theory and Applications, 2022, 31(6): 783-792. DOI: 10.15953/j.ctta.2022.041. (in Chinese).

Citation:

PDF (3528 KB)

Metal Artifact Reduction Algorithm for CT Images of Rock and Mineral Samples Based on Dual-domain Adaptive Network

ZUO Shunji^1,,
FENG Peng^{1, 2},
HUANG Pan¹,
YAN Shenghao¹,
HE Peng^{1, 2},
WEI Biao^{1, 2, ,}

1.
Key Laboratory of Optoelectronic Technology and System, Ministry of Education, Chongqing University, Chongqing 400044, China
2.
Engineering Research Center of Industrial CT Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China

More Information

Received Date: March 10, 2022
Revised Date: May 31, 2022
Accepted Date: June 06, 2022
Available Online: June 27, 2022
Published Date: November 02, 2022

Graphical Abstract

Abstract

Abstract

Rock and mineral samples contain a large amount of high-density metal substances, which often lead to metal artifacts in CT images and seriously affect the parameters analysis accuracy of rock and mineral samples. In order to improve the quality of CT images and suppress metal artifacts, in this paper we propose a dual-domain adaptive network-based metal artifact reduction algorithm for CT images of rock samples (DDA-CNN-MAR). The metal artifacts are suppressed by the projection domain network and the image domain network successively, and the dual domain processing results are adaptively fused to realize the end-to-end mapping from images with artifacts to artifact-free images. The algorithm is based on the residual encoder-decoder network model (RED-CNN), which is easy to extract features and restore image details. The dual-domain structure can adaptively adjust the weights of the projection domain (artifact suppression) and image domain (detail inpainting) to obtain optimal reduction results. The experimental results show that, compared with the RED-CNN metal artifact denoising method in the image domain, the MSE of the image corrected by the DDA-CNN-MAR method is reduced by 2.570, while the PSNR and SSIM are increased by 1.218 dB and 0.018 respectively, which effectively improves the CT image quality.
- industrial CT,
- deep learning,
- reduction algorithms,
- metal rock samples,
- metal artifacts

FullText(HTML)

References (17)

References

[1]	何鹏, 魏彪, 陈超, 等. 华南成矿省福建魁歧晶洞花岗岩样品孔隙结构的工业X-CT三维可视化研究[J]. 地质学报, 2014,88(4): 777−783. HE P, WEI B, CHEN C, et al. The three-dimensional visualization study on pore structure of Fujian Kuiqi geode granite sample in South China metallogenic belt based on industrial computed tomography[J]. Acta Geologica Sinica, 2014, 88(4): 777−783. (in Chinese).
[2]	TSAFNAT N, TSAFNAT G, JONES A S. Automated mineralogy using finite element analysis and X-ray microtomography[J]. Minerals Engineering, 2009, 22(2): 149−155. doi: 10.1016/j.mineng.2008.06.003
[3]	陈慧娟. CT系统中射束硬化校正算法研究[D]. 太原: 中北大学, 2010. CHEN H J. The algorithms study of beam-hardening correction in CT[D]. Taiyuan: North University of China, 2010. (in Chinese).
[4]	陈超, 魏彪, 梁婷, 等. 一种基于工业CT技术的岩芯样品孔隙度测量分析方法[J]. 物探与化探, 2013,37(3): 500−507. CHEN C, WEI B, LIANG T. et al. The application of industrial computation tomography (CT) to the analysis of core sample porosity[J]. Geophysical & Geochemical Exploration, 2013, 37(3): 500−507. (in Chinese).
[5]	肖文, 曾理. CT图像的金属伪影校正方法综述[J]. 中国体视学与图像分析, 2019,(1): 29−36. doi: 10.13505/j.1007-1482.2019.24.01.004 XIAO W, ZENG L. The review of metal artifact reduction for CT images[J]. Chinese Journal of Stereology and Image Analysis, 2019, (1): 29−36. (in Chinese). doi: 10.13505/j.1007-1482.2019.24.01.004
[6]	GJESTEBY L, MAN B D, JIN Y, et al. Metal artifact reduction in CT: Where are we after four decades?[J]. IEEE Access, 2016, 4: 5826−5849. doi: 10.1109/ACCESS.2016.2608621
[7]	CARON M, BOJANOWSKI P, JOULIN A, et al. Deep clustering for unsupervised learning of visual features[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 132-149.
[8]	REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. Advances in Neural Information Processing Systems, 2015, 28: 91−99.
[9]	TANG Y, HUANG J, ZHANG F, et al. Deep residual networks with a fully connected reconstruction layer for single image super-resolution[J]. Neurocomputing, 2020, 405: 186−199. doi: 10.1016/j.neucom.2020.04.030
[10]	GJESTEBY L, YANG Q, XI Y, et al. Deep learning methods for CT image-domain metal artifact reduction[C]//Developments in X-ray Tomography XI. International Society for Optics and Photonics, 2017, 10391: 103910W.
[11]	马燕, 钟发生, 刘丰林. 基于条件生成式对抗网络的CT金属伪影校正研究[J]. 中国体视学与图像分析, 2021,26(2): 101−112. doi: 10.13505/j.1007-1482.2021.26.02.001 MA Y, ZHONG F S, LIU F L. CT metal artifact reduction based on conditional generative adversarial network[J]. Chinese Journal of Stereology and Image Analysis, 2021, 26(2): 101−112. (in Chinese). doi: 10.13505/j.1007-1482.2021.26.02.001
[12]	ZHANG Y, YU H. Convolutional neural network based metal artifact reduction in X-ray computed tomography[J]. IEEE Transactions on Medical Imaging, 2018, 37(6): 1370−1381. doi: 10.1109/TMI.2018.2823083
[13]	LIAO H, LIN W A, ZHOU S K, et al. ADN: Artifact disentanglement network for unsupervised metal artifact reduction[J]. IEEE Transactions on Medical Imaging, 2019, 39(3): 634−643.
[14]	马燕, 余海军, 钟发生, 等. 基于残差编解码网络的CT图像金属伪影校正[J]. 仪器仪表学报, 2020,41(8): 160−169. doi: 10.19650/j.cnki.cjsi.J2006503 MA Y, YU H J, ZHONG F S, et al. CT metal artifact reduction based on the residual encoder-decoder network[J]. Chinese Journal of Scientific Instrument, 2020, 41(8): 160−169. (in Chinese). doi: 10.19650/j.cnki.cjsi.J2006503
[15]	LIN W A, LIAO H, PENG C, et al. Dudonet: Dual domain network for ct metal artifact reduction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 10512-10521.
[16]	CHEN H, ZHANG Y, KALRA M K, et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE Transactions on Medical Imaging, 2017, 36(12): 2524−2535. doi: 10.1109/TMI.2017.2715284
[17]	KALENDER W A, HEBEL R, EBERSBERGER J. Reduction of CT artifacts caused by metallic implants[J]. Radiology, 1987, 164(2): 576−577. doi: 10.1148/radiology.164.2.3602406

[1]	SU Danyang, HOU Ping, ZHANG Haoran, MA Yuanbo, LIU Jinlong, YANG Xiaopeng. Research Progress on Reducing Metal Artifacts in CT Imaging[J]. CT Theory and Applications, 2025, 34(3): 499-505. DOI: 10.15953/j.ctta.2024.023
[2]	HU Yongzhi, WU Pengfei, CAO Da, WANG Binbin, TANG Yuxia, WANG Shouju, WANG Chuanbing. Elucidation of the Efficacy of Low Tube Voltage Combined with AIIR in Suppressing Hip Prosthesis CT Artifacts[J]. CT Theory and Applications, 2025, 34(3): 377-384. DOI: 10.15953/j.ctta.2024.334
[3]	CHENG Tianxin, ZHANG Yongxian. Orthopedic Metal Artifact Reduction Combined with Iterative Reconstruction in CT Imaging after Lumbar Internal Fixation[J]. CT Theory and Applications, 2025, 34(2): 285-293. DOI: 10.15953/j.ctta.2024.091
[4]	CHEN Zonggui, WEI Ningning, LI Zhoufan, PAN Guihong, HUANG Minjiang. Methods Research on Correction for Intracranial Metal Artifacts Based on Prior Image Utilization[J]. CT Theory and Applications, 2025, 34(1): 141-148. DOI: 10.15953/j.ctta.2024.002
[5]	WEI Pengyue, HUANG Xiaoying, WU Tong, GUO Fangkai, GUAN Jing, BAO Yunfeng. Effect of denture materials on CT image artifacts: phantom experiment[J]. CT Theory and Applications. DOI: 10.15953/j.ctta.2024.339
[6]	ZHANG Wenjun, HUANG Gang, DING Haining, XU Hongchun. Research Progress of Scattering Artifact Correction in Medical Cone-beam Computed Tomography Imaging Based on Deep Learning[J]. CT Theory and Applications, 2023, 32(2): 285-296. DOI: 10.15953/j.ctta.2022.131
[7]	ZHAO Qingqing, SUN Xiaoli, LI Xi, WANG Yanwen, LIU Weina, DUAN Yongli, WANG Rengui. Utility of Spectral CT Monochromatic Imaging with Metal Artifacts Reduction (MAR) for the Reduction of Metal Artifacts of Embolization Coil Implants[J]. CT Theory and Applications, 2019, 28(5): 529-539. DOI: 10.15953/j.1004-4140.2019.28.05.02
[8]	ZHOU Li-ping, SUN Yi, CHENG Kai, YU Jian-qiao. Deep Learning Based Beam Hardening Artifact Reduction in Industrial X-ray CT[J]. CT Theory and Applications, 2018, 27(2): 227-240. DOI: 10.15953/j.1004-4140.2018.27.02.11
[9]	CHAI Jia-bin, LI Gong-ping, WEI Cun-feng, WEI Long, YU Yang, WANG Qian, LIU Bao-dong. Metal Artifacts Correction Method Research about Liquid Metal Angiography[J]. CT Theory and Applications, 2016, 25(2): 149-157. DOI: 10.15953/j.1004-4140.2016.25.02.04
[10]	LI Qing-liang, YAN Bin, SUN Hong-sheng, LI Lei, ZHANG Feng. Review of Metal Artifacts Correction Methods on X-ray Computed Tomography[J]. CT Theory and Applications, 2011, 20(1): 131-140.

Cited By

Cited by

Periodical cited type(1)

周丽，王晖，胡剑. 探讨CDFI、CT、MRI诊断睾丸扭转的价值与病理学一致性分析. 中国CT和MRI杂志. 2024(03): 152-154 .

Other cited types(0)

Get Citation

PDF

XML

Article views (596) PDF downloads (226) Cited by(1)

Metal Artifact Reduction Algorithm for CT Images of Rock and Mineral Samples Based on Dual-domain Adaptive Network

Abstract

References

Related Articles

Cited by

Periodical cited type(1)

Other cited types(0)

Catalog

Related

Metal Artifact Reduction Algorithm for CT Images of Rock and Mineral Samples Based on Dual-domain Adaptive Network

Abstract

References

Related Articles

Cited by

Periodical cited type(1)

Other cited types(0)

Catalog

Related

Export File

Citation

Format

Content