[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Ring artifact removal for differential phase-contrast X-ray computed tomography using a conditional generative adversarial network

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The integration process used as a pre-processing step in the reconstruction of differential phase-contrast X-ray CT (d-PCCT) causes the measurement noise to propagate throughout the projection image, which is leading to increased ring artifacts (RA) in the reconstructed image. It is difficult to eliminate the RA using conventional RA removal methods that were developed for the absorption-based CT field. We propose an effective method that can remove RA of d-PCCT images.

Methods

The proposed method uses Laplacian images reconstructed from second-derivative projections of d-PCCT. This method is based on a conditional generative adversarial network (cGAN), whose loss function is designed by adding the L1- and L2-norm to the original cGAN. The training data were taken from a numerical phantom generated by a d-PCCT imaging simulator. To validate the applicability of the trained network, we tested its RA removal effect on test data from numerical phantoms generated randomly and actual experimental data.

Results

The results of numerical validation using numerical phantoms showed that the proposed method improved the RA removal effect compared to conventional methods. In addition, image comparison by visual evaluation showed that only the proposed method was able to remove RA while preserving original structures in the actual biological d-PCCT images.

Conclusion

We proposed a cGAN-based method for RA removal that exploits the physical properties of d-PCCT. The proposed method was able to completely remove RA from d-PCCT images on both simulated data and biological data. We believe that this method is useful for the observation of various types of biological soft tissue.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Wu J, Takeda T, Lwin TT, Momose A, Sunaguchi N, Fukami T, Yuasa T, Akatsuka T (2009) Imaging renal structures by X-ray phase-contrast microtomography. Kidney Int 75(9):945–951. https://doi.org/10.1038/ki.2009.42

    Article  PubMed  Google Scholar 

  2. Saccomano M, Albers J, Tromba G, Dobrivojević Radmilović M, Gajović S, Alves F, Dullin C (2018) Synchrotron inline phase contrast µCT enables detailed virtual histology of embedded soft-tissue samples with and without staining. J Synchrotron Radiat 25(4):1153–1161. https://doi.org/10.1107/S1600577518005489

    Article  CAS  PubMed  Google Scholar 

  3. Reichardt M, Töpperwien M, Khan A, Alves F, Salditt T (2020) Fiber orientation in a whole mouse heart reconstructed by laboratory phase-contrast micro-CT. J Med Imaging 7(2):023501. https://doi.org/10.1117/1.JMI.7.2.023501

    Article  Google Scholar 

  4. Sunaguchi N, Shimao D, Yuasa T, Ichihara S, Nishimura R, Oshima R, Watanabe A, Niwa K, Ando M (2020) Three-dimensional microanatomy of human nipple visualized by X-ray dark-field computed tomography. Breast Cancer Res Treat 180(2):397–405. https://doi.org/10.1007/s10549-020-05574-w

    Article  PubMed  Google Scholar 

  5. Snigirev A, Snigireva I, Kohn V, Kuznetsov S, Schelokov I (1995) On the possibilities of X-ray phase contrast microimaging by coherent high-energy synchrotron radiation. Rev Sci Instrum 66(12):5486–5492. https://doi.org/10.1063/1.1146073

    Article  CAS  Google Scholar 

  6. Momose A, Takeda T, Itai Y, Hirano K (1996) Phase-contrast X-ray computed tomography for observing biological soft tissues. Nat Med 2(4):473–475. https://doi.org/10.1038/nm0496-473 (Erratum. Nat Med 2, 596 (1996). https://doi.org/10.1038/nm0596-596)

  7. Chapman D, Thomlinson W, Johnston RE, Washburn D, Pisano E, Gmür N, Zhong Z, Menk R, Arfelli F, Sayers D (1997) Diffraction enhanced X-ray imaging. Phys Med Biol 42(11):2015–2025. https://doi.org/10.1088/0031-9155/42/11/001

    Article  CAS  PubMed  Google Scholar 

  8. Suortti P, Keyriläinen J, Thomlinson W (2013) Analyser-based X-ray imaging for biomedical research. J Phys D Appl Phys 46(49):494002. https://doi.org/10.1088/0022-3727/46/49/494002

    Article  CAS  Google Scholar 

  9. Ando M, Maksimenko A, Sugiyama H, Pattanasiriwisawa W, Hyodo K, Uyama C (2002) Simple X-ray dark- and bright-field imaging using achromatic Laue optics. Jpn J Appl Phys 41(9A):L1016–L1018

    Article  CAS  Google Scholar 

  10. Momose A, Kawamoto S, Koyama I, Hamaishi Y, Takai K, Suzuki Y (2003) Demonstration of X-ray Talbot interferometry. Jpn J Appl Phys 42(7B):L866–L868. https://doi.org/10.1143/jjap.42.l866

    Article  CAS  Google Scholar 

  11. Raven C (1998) Numerical removal of ring artifacts in microtomography. Rev Sci Instrum 69(8):2978–2980. https://doi.org/10.1063/1.1149043

    Article  CAS  Google Scholar 

  12. Münch B, Trtik P, Marone F, Stampanoni M (2009) Stripe and ring artifact removal with combined wavelet—Fourier filtering. Opt Express 17(10):8567–8591. https://doi.org/10.1364/OE.17.008567

    Article  CAS  PubMed  Google Scholar 

  13. Boin M, Haibel A (2006) Compensation of ring artefacts in synchrotron tomographic images. Opt Express 14(25):12071–12075. https://doi.org/10.1364/OE.14.012071

    Article  PubMed  Google Scholar 

  14. Kim Y, Baek J, Hwang D (2014) Ring artifact correction using detector line-ratios in computed tomography. Opt Express 22(11):13380–13392. https://doi.org/10.1364/OE.22.013380

    Article  PubMed  Google Scholar 

  15. Prell D, Kyriakou Y, Kalender WA (2009) Comparison of ring artifact correction methods for flat-detector CT. Phys Med Biol 54(12):3881–3895. https://doi.org/10.1088/0031-9155/54/12/018

    Article  PubMed  Google Scholar 

  16. Chen Y, Duan G (2009) Independent component analysis based ring artifact reduction in cone-beam CT images. In: 2009 16th IEEE international conference on image processing (ICIP), pp 4189–4192. https://doi.org/10.1109/ICIP.2009.5414528

  17. Liang X, Zhang Z, Niu T, Yu S, Wu S, Li Z, Zhang H, Xie Y (2017) Iterative image-domain ring artifact removal in cone-beam CT. Phys Med Biol 62(13):5276–5292. https://doi.org/10.1088/1361-6560/aa7017

    Article  PubMed  Google Scholar 

  18. Zhao S, Li J, Huo Q (2018) Removing ring artifacts in CBCT images via generative adversarial network. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1055–1059. https://doi.org/10.1109/ICASSP.2018.8462316

  19. Wang Z, Li J, Enoh M (2019) Removing ring artifacts in CBCT images via generative adversarial networks with unidirectional relative total variation loss. Neural Comput Appl 31(9):5147–5158. https://doi.org/10.1007/s00521-018-04007-6

    Article  Google Scholar 

  20. Sunaguchi N, Yuasa T, Gupta R, Ando M (2015) An efficient reconstruction algorithm for differential phase-contrast tomographic images from a limited number of views. Appl Phys Lett 107(25):253701. https://doi.org/10.1063/1.4938211

    Article  CAS  Google Scholar 

  21. Sunaguchi N, Yuasa T, Sun F, Gupta R, Ando M (2015) Limited view reconstruction for differential phase-contrast computed tomography. Opt Express 23(8):9717–9729. https://doi.org/10.1364/OE.23.009717

    Article  PubMed  Google Scholar 

  22. Isola P, Zhu J, Zhou T, Efros AA (2017) Image-to-Image translation with conditional adversarial Networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1125–1134. https://doi.org/10.1109/CVPR.2017.632

  23. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge

    Google Scholar 

  24. ESRF (2013) XOP2.3 software. ESRF Anonymous FTP Server. ftp://ftp.esrf.eu/scisoft/xop2.3/DabaxFiles/ Accessed 25 Feb 2021

  25. Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal 38(2):295–307. https://doi.org/10.1109/TPAMI.2015.2439281

    Article  Google Scholar 

  26. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The experiments were performed as part of four research projects (2008S2002, 2016G0625, 2012G562, 2015G597, 2020G583) funded by the KEK. This research was partially supported by a Grant-in-Aid for Scientific Research (Grant Number 18K13765) from the Japanese Ministry of Education, Culture, Sports, Science and Technology and “Knowledge Hub Aichi”, Priority Research Project from Aichi Prefectural Government.

Funding

This research was partially supported by a Grant-in-Aid for Scientific Research (Grant Number 18K13765) from the Japanese Ministry of Education, Culture, Sports, Science and Technology and “Knowledge Hub Aichi,” Priority Research Project from Aichi Prefectural Government.

Author information

Authors and Affiliations

  1. Department of Radiological and Medical Laboratory Sciences, Graduate School of Medicine, Nagoya University, Nagoya, Japan

    Zhuoran Huang & Naoki Sunaguchi

  2. Department of Radiological Technology, Hokkaido University of Science, Sapporo, Japan

    Daisuke Shimao

  3. Department of Pathology, Graduate School of Medicine, Nagoya University, Nagoya, Japan

    Atsushi Enomoto

  4. Department of Pathology, Nagoya Medical Center, Nagoya, Japan

    Shu Ichihara

  5. Graduate School of Engineering and Science, Yamagata University, Yonezawa, Japan

    Tetsuya Yuasa

  6. High Energy Accelerator Research Organization, Tsukuba, Japan

    Masami Ando

Authors
  1. Zhuoran Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naoki Sunaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daisuke Shimao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Atsushi Enomoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shu Ichihara
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tetsuya Yuasa
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Masami Ando
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naoki Sunaguchi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

All experimental protocols were approved by the Animal Care and Use Committee of Nagoya University Graduate School of Medicine.

Informed consent

This article does not contain patient data.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Z., Sunaguchi, N., Shimao, D. et al. Ring artifact removal for differential phase-contrast X-ray computed tomography using a conditional generative adversarial network. Int J CARS 16, 1889–1900 (2021). https://doi.org/10.1007/s11548-021-02500-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-021-02500-3

Keywords

Search

Navigation