Volume 37 Issue 12
Nov.  2025
Turn off MathJax
Article Contents
Li Fengxiao, Yang Run, Sun Zhiqiang, et al. High-resolution reconstruction algorithm for high-density workpiece inspection in Rhodotron-based industrial CT[J]. High Power Laser and Particle Beams, 2025, 37: 124004. doi: 10.11884/HPLPB202537.250263
Citation: Li Fengxiao, Yang Run, Sun Zhiqiang, et al. High-resolution reconstruction algorithm for high-density workpiece inspection in Rhodotron-based industrial CT[J]. High Power Laser and Particle Beams, 2025, 37: 124004. doi: 10.11884/HPLPB202537.250263

High-resolution reconstruction algorithm for high-density workpiece inspection in Rhodotron-based industrial CT

doi: 10.11884/HPLPB202537.250263
  • Received Date: 2025-08-18
  • Accepted Date: 2025-10-11
  • Rev Recd Date: 2025-10-11
  • Available Online: 2025-10-23
  • Publish Date: 2025-11-06
  • Background
    High-resolution industrial computed tomography (CT) is crucial for the non-destructive testing (NDT) of critical components, particularly in the aerospace industry where high-density materials are common. The Rhodotron accelerator, with its micro-focus capability, offers a hardware advantage for achieving high spatial resolution over traditional linear accelerators. However, its potential is severely hampered when inspecting large, high-density workpieces. The strong X-ray attenuation leads to projection data with a very low signal-to-noise ratio (SNR), causing conventional reconstruction algorithms to either produce noisy images or oversmooth critical details, thereby limiting the system’s effective resolution.
    Purpose
    This study aims to develop and validate a reconstruction algorithm capable of overcoming the low-SNR challenge inherent in Rhodotron CT scans of high-density objects. The primary objective is to achieve high-resolution, high-fidelity image reconstruction that effectively suppresses noise while preserving the fine structural edges essential for accurate defect detection.
    Methods
    A novel iterative algorithm, termed Projection Onto Convex Sets regularized by Bilateral Total Variation (POCS-BTV), is proposed. The algorithm integrates BTV, a regularizer known for its superior edge-preservation properties, into the POCS framework to constrain the solution during iterations. The performance of POCS-BTV was evaluated against the Simultaneous Iterative Reconstruction Technique (SIRT), POCS-TV, and POCS-RTV algorithms. The evaluation involved two stages: a simulation experiment using a Shepp-Logan phantom with added Poisson-Gaussian noise to mimic low-SNR conditions, and a physical experiment using a 70 mm diameter high-strength steel wire rope phantom scanned by a 9 MeV Rhodotron accelerator CT system.
    Results
    In the simulation experiment, the POCS-BTV algorithm demonstrated superior quantitative performance, achieving a Peak Signal-to-Noise Ratio (PSNR) of 30.76 and a Structural Similarity Index (SSIM) of 0.8405, which were significantly better than the comparison algorithms. In the real data experiment, visual analysis of the reconstructed images showed that POCS-BTV successfully resolved the fine gaps between individual steel wires. This was in stark contrast to other methods, which suffered from structural aliasing and blurred edges due to noise.
    Conclusions
    The POCS-BTV algorithm effectively unlocks the high-resolution potential of the Rhodotron accelerator hardware, even in challenging low-SNR scenarios. By achieving an optimal balance between noise suppression and detail preservation, it provides a robust and reliable solution for the precision NDT of critical high-density industrial components, demonstrating significant value for practical engineering applications.
  • loading
  • [1]
    Withers P J, Bouman C, Carmignato S, et al. X-ray computed tomography[J]. Nature Reviews Methods Primers, 2021, 1: 18. doi: 10.1038/s43586-021-00015-4
    [2]
    He Xiaozhong, Liao Shuqing, Long Jidong, et al. A proposal of using improved Rhodotron as a high dose rate micro-focused X-ray source[C]//Proceedings of the 13th Symposium on Accelerator Physics (SAP 17). 2017: 28-30.
    [3]
    He Xiaozhong, Yang Liu, Liao Shuqing, et al. Rhodotron and rotating target: a solution towards micro-spot for high energy and high dose rate bremsstrahlung sources[J]. Applied Radiation and Isotopes, 2022, 189: 110446. doi: 10.1016/j.apradiso.2022.110446
    [4]
    Balda M, Hornegger J, Heismann B. Ray contribution masks for structure adaptive sinogram filtering[J]. IEEE Transactions on Medical Imaging, 2012, 31(6): 1228-1239. doi: 10.1109/TMI.2012.2187213
    [5]
    Xie Q, Zeng D, Zhao Q, et al. Robust low-dose CT sinogram preprocessing via exploiting noise-generating mechanism[J]. IEEE Transactions on Medical Imaging, 2017, 36(12): 2487-2498. doi: 10.1109/TMI.2017.2767290
    [6]
    Gui Zhiguo, Liu Yi. Noise reduction for low-dose X-ray computed tomography with fuzzy filter[J]. Optik, 2012, 123(13): 1207-1211. doi: 10.1016/j.ijleo.2011.07.052
    [7]
    蔡玉芳, 陈桃艳, 王珏, 等. 基于自适应滤波系数的非局部均值计算机层析成像的图像降噪方法[J]. 光学学报, 2020, 40: 071001

    Cai Yufang, Chen Taoyan, Wang Jue, et al. Image noise reduction in computed tomography with non-local means algorithm based on adaptive filtering coefficients[J]. Acta Optica Sinica, 2020, 40: 071001
    [8]
    龙超, 金恒, 黎玲, 等. 基于特征融合的非局部均值CT图像降噪[J]. 光学学报, 2022, 42: 1134024 doi: 10.3788/AOS202242.1134024

    Long Chao, Jin Heng, Li Ling, et al. CT image denoising with non-local means based on feature fusion[J]. Acta Optica Sinica, 2022, 42: 1134024 doi: 10.3788/AOS202242.1134024
    [9]
    Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095. doi: 10.1109/TIP.2007.901238
    [10]
    Zhong Hua, Ma Ke, Zhou Yang. Modified BM3D algorithm for image denoising using nonlocal centralization prior[J]. Signal Processing, 2015, 106: 342-347. doi: 10.1016/j.sigpro.2014.08.014
    [11]
    Sidky E Y, Pan Xiaochuan. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization[J]. Physics in Medicine & Biology, 2008, 53(17): 4777-4807.
    [12]
    Sidky E Y, Duchin Y, Pan Xiaochuan, et al. A constrained, total-variation minimization algorithm for low-intensity X-ray CT[J]. Medical Physics, 2011, 38(S1): S117-S125. doi: 10.1118/1.3560887
    [13]
    Xu Li, Yan Qiong, Xia Yan, et al. Structure extraction from texture via relative total variation[J]. ACM Transactions on Graphics, 2012, 31: 139.
    [14]
    Gong Changcheng, Zeng Li. Adaptive iterative reconstruction based on relative total variation for low-intensity computed tomography[J]. Signal Processing, 2019, 165: 149-162. doi: 10.1016/j.sigpro.2019.06.031
    [15]
    He Lei, Xie Yongfang, Xie Shiwen, et al. Structure-preserving texture smoothing via scale-aware bilateral total variation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(4): 1493-1506. doi: 10.1109/TCSVT.2022.3214219
    [16]
    Chen Hongchi, Li Qiuxia, Zhou Lazhen, et al. Deep learning-based algorithms for low-dose CT imaging: a review[J]. European Journal of Radiology, 2024, 172: 111355. doi: 10.1016/j.ejrad.2024.111355
    [17]
    Ma Limin, Yao Yudong, Teng Yueyang. Iterator-net: sinogram-based CT image reconstruction[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13050-13061. doi: 10.3934/mbe.2022609
    [18]
    Combettes P L, Wajs V R. Signal recovery by proximal forward-backward splitting[J]. Multiscale Modeling & Simulation, 2005, 4(4): 1168-1200.
    [19]
    Hale E T, Yin Wotao, Zhang Yin. Fixed-point continuation for 1-minimization: methodology and convergence[J]. SIAM Journal on Optimization, 2008, 19(3): 1107-1130. doi: 10.1137/070698920
    [20]
    Tian Zhen, Jia Xun, Yuan Kehong, et al. Low-dose CT reconstruction via edge-preserving total variation regularization[J]. Physics in Medicine & Biology, 2011, 56(18): 5949-5967.
    [21]
    Krishnan D, Szeliski R. Multigrid and multilevel preconditioners for computational photography[J]. ACM Transactions on Graphics (TOG), 2011, 30(6): 1-10.
    [22]
    Liu Yan, Ma Jianhua, Fan Yi, et al. Adaptive-weighted total variation minimization for sparse data toward low-dose X-ray computed tomography image reconstruction[J]. Physics in Medicine & Biology, 2012, 57(23): 7923-7956.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(4)

    Article views (117) PDF downloads(10) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return