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Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT

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Abstract

Limiting the potential risks associated with radiation exposure is critically important when obtaining a diagnostic image. However, lowering the level of radiation may cause excessive noise and artifacts in computed tomography (CT) scans. In this study, we implemented and tested the performance of patch-based and block-based REDCNN models and revealed that a 3D kernel is efficient in removing 3D noise and artifacts. Additionally, we applied a 3D bilateral filter and a 2D-based Landweber iteration method to remove any remaining noise and to prevent the edges from blurring, which are limitations of a deep learning-based noise reduction system. For the 2D-based Landweber iteration, we examined the requisite step size and the number of iterations. The representative CT noise and artifacts, which were Gaussian noise and view aliasing artifacts, respectively, were simulated on XCAT and reproduced in vivo to verify that the proposed method could be used in an analogous clinical setting. Lastly, the performance of the proposed algorithm was evaluated on in vivo data with real low-dose noise. Our proposed method effectively suppressed complex noise without losing diagnostic features in both the simulation study and experimental evaluation. Furthermore, for the simulation study, we adopted a numerical observer model to evaluate the structural fidelity of the image quality more appropriately than existing image quality assessment methods.

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References

  1. Brenner, D.J., Hall, E.J.: Current concepts—computed tomography—an increasing source of radiation exposure. New Engl. J. Med. 357, 2277–2284 (2007)

    Article  Google Scholar 

  2. Pearce, M.S., et al.: Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. Lancet 380, 499–505 (2012)

    Article  Google Scholar 

  3. Wang, J., et al.: An experimental study on the noise properties of x-ray CT sinogram data in Radon space. Phys. Med. Biol. 53, 3327–3341 (2008)

    Article  Google Scholar 

  4. Balda, M., Hornegger, J., Heismann, B.: Ray contribution masks for structure adaptive sinogram filtering. IEEE Trans. Med. Imag. 31, 1228–1239 (2012)

    Article  Google Scholar 

  5. Wang, J., Li, T., Lu, H., Liang, Z.: Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography. IEEE Trans. Med. Imag. 25, 1272–1283 (2006)

    Article  Google Scholar 

  6. Sidky, E.Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys. Med. Biol. 53, 4777–4807 (2008)

    Article  Google Scholar 

  7. Chen, Y., et al.: Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior. Comput. Med. Imag. Grap. 33, 495–500 (2009)

    Article  Google Scholar 

  8. Xu, Q., Yu, H.Y., Mou, X.Q., Zhang, L., Hsieh, J., Wang, G.: Low-dose X-ray CT reconstruction via dictionary learning. IEEE Trans. Med. Imag. 31, 1682–1697 (2012)

    Article  Google Scholar 

  9. Cai, J.F., Jia, X., Gao, H., Jiang, S.B., Shen, Z.W., Zhao, H.K.: Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study. IEEE Trans. Med. Imag. 33, 1581–1591 (2014)

    Article  Google Scholar 

  10. Shan, H., Wang, G., Yang, Y.: Accelerated correction of reflection artifacts by deep neural networks in photo-acoustic tomography. Appl. Sci. 9, 2615 (2019)

    Article  Google Scholar 

  11. Landweber, L.: An iteration formula for Fredholm integral equations of the first kind. Am. J. Math. 73, 615–624 (1951)

    Article  MathSciNet  Google Scholar 

  12. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)

    Article  Google Scholar 

  13. Li, Z., et al.: Adaptive nonlocal means filtering based on local noise level for CT denoising. Med. Phys. 41, 011908 (2014)

    Article  Google Scholar 

  14. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process 16, 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  15. Fumene Feruglio, P., Vinegoni, C., Gros, J., Sbarbati, A., Weissleder, R.: Block matching 3D random noise filtering for absorption optical projection tomography. Phys. Med. Biol. 55, 5401–5415 (2010)

    Article  Google Scholar 

  16. Sheng, K., Gou, S., Wu, J., Qi, S.X.: Denoised and texture enhanced MVCT to improve soft tissue conspicuity. Med. Phys. 41, 101916 (2014)

    Article  Google Scholar 

  17. Kang D, et al.: Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm. In: Proceedings of Spie 8669

  18. Chen H, et al.: Low-dose CT denoising with convolutional neural network. In: Proceedings of 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017)

  19. Badretale S, Shaker F, Babyn P, Alirezaie J: Deep convolutional approach for low-dose CT image noise reduction. In: Proceedings of 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)

  20. Chen, H., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imag. 36, 2524–2535 (2017)

    Article  Google Scholar 

  21. Yang, W., et al.: Improving low-dose CT image using residual convolutional network. IEEE Access 5, 24698–24705 (2017)

    Article  Google Scholar 

  22. Kang, E., Chang, W., Yoo, J., Ye, J.C.: Deep convolutional framelet denosing for low-dose CT via wavelet residual network. IEEE Trans. Med. Imag. 37, 1358–1369 (2018)

    Article  Google Scholar 

  23. Shan, H., et al.: 3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network. IEEE Trans. Med. Imag. 37, 1522–1534 (2018)

    Article  Google Scholar 

  24. Yang, Q., Yan, P., Kalra, M.K., Wang, G.: CT image denoising with perceptive deep neural networks (2017). aXriv:1702.07019

  25. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). aXriv:1409.1556

  26. Goodfellow, I.J., et al.: Generative adversarial nets. Adv Neur In 27 (2014)

  27. Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imag. 37, 1348–1357 (2018)

    Article  Google Scholar 

  28. Yi, X., Babyn, P.: Sharpness-aware low-dose CT denoising using conditional generative adversarial network. J. Digit. Imag. 31, 655–669 (2018)

    Article  Google Scholar 

  29. Ran, M.S., et al.: Denoising of 3D magnetic resonance images using a residual encoder-decoder Wasserstein generative adversarial network. Med. Image Anal. 55, 165–180 (2019)

    Article  Google Scholar 

  30. You, C., et al.: Structurally-sensitive multi-scale deep neural network for low-dose CT denoising. IEEE Access 6, 41839–41855 (2018)

    Article  Google Scholar 

  31. Yuan, H.Z., Jia, J.Z., Zhu, Z.X.: Sipid: a deep learning framework for sinogram interpolation and image denoising in low-dose Ct reconstruction. I S Biomed. Imag. 1521–1524, (2018)

  32. Han, Y.S., Yoo, J., Ye, J.C.: Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis. (2016). aXriv:1611.06391

  33. Zhao, T., McNitt-Gray, M., Ruan, D.: A convolutional neural network for ultra-low-dose CT denoising and emphysema screening. Med. Phys. 46, 3941–3950 (2019)

    Article  Google Scholar 

  34. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for Image denoising. IEEE Trans. Image Process 26, 3142–3155 (2017)

    Article  MathSciNet  Google Scholar 

  35. Kim, B., Han, M., Shim, H., Baek, J.: A performance comparison of convolutional neural network-based image denoising methods: the effect of loss functions on low-dose CT images. Med. Phys. 46, 3906–3923 (2019)

    Article  Google Scholar 

  36. Chun, I.Y., Zheng, X., Long, Y., Fessler, J.A.: BCD-Net for low-dose CT reconstruction: acceleration, convergence, and generalization. In: Proceedings of international conference on medical image computing and computer-assisted intervention

  37. Choi, D., et al.: Multidimensional noise reduction in C-arm cone-beam CT via 2D-based Landweber iteration and 3D-based deep neural networks. In: Proceedings of Medical Imaging 2019: Physics of Medical Imaging

  38. Segars, W.P., Sturgeon, G., Mendonca, S., Grimes, J., Tsui, B.M.: 4D XCAT phantom for multimodality imaging research. Med. Phys. 37, 4902–4915 (2010)

    Article  Google Scholar 

  39. Maier, A.K., et al.: CONRAD—a software framework for cone-beam imaging in radiology. Med. Phys. (2013)

  40. Choi, J.H., et al.: Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. Part I. Numerical model-based optimization. Med. Phys. 40 (2013)

  41. Schwemmer, C: High-density object removal from X-ray projection images. Universität Erlangen-Nürnberg, Diplomarbeit in Computer Science, Master's (01.07.2010 2010)

  42. Zellerhoff, M., Scholz, B., Ruhrnschopf, E.P., Brunner, T.: Low contrast 3D-reconstruction from C-arm data. Medical Imaging 2005: Physics of Medical Imaging, Pts 1 and 2 5745:646–655 (2005)

  43. Ruhrnschopf, E.P., Klingenbeck, K.: A general framework and review of scatter correction methods in x-ray cone-beam computerized tomography. Part 1: scatter compensation approaches. Med. Phys. 38, 4296–4311 (2011)

    Article  Google Scholar 

  44. Feldkamp, L.A., Davis, L.C., Kress, J.W.: Practical cone-beam algorithm. Josa a 1, 612–619 (1984)

    Article  Google Scholar 

  45. Manhart, M.T., et al.: Dynamic iterative reconstruction for interventional 4-D C-arm CT perfusion imaging. IEEE Trans. Med. Imag. 32, 1336–1348 (2013)

    Article  Google Scholar 

  46. Gravel, P., Beaudoin, G., De Guise, J.A.: A method for modeling noise in medical images. IEEE Trans. Med. Imag. 23, 1221–1232 (2004)

    Article  Google Scholar 

  47. Lu, H.B., Li, X., Hsiao, I.T., Liang, Z.G.: Analytical noise treatment for low-dose CT projection data by penalized weighted least-square smoothing in the K-L domain. P Soc. Photo-Opt. Ins. 4682, 146–152 (2002)

    Google Scholar 

  48. Barrett, J.F., Keat, N.: Artifacts in CT: recognition and avoidance. Radiographics 24, 1679–1691 (2004)

    Article  Google Scholar 

  49. Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: Proceedings of 2010 20th International Conference on Pattern Recognition

  50. He, X., Park, S.: Model observers in medical imaging research. Theranostics 3, 774–786 (2013)

    Article  Google Scholar 

  51. Abbey, C.K., Barrett, H.H.: Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 18, 473–488 (2001)

    Article  Google Scholar 

  52. Park, S., Badano, A., Gallas, B.D., Myers, K.J.: Incorporating human contrast sensitivity in model observers for detection tasks. IEEE Trans. Med. Imag. 28, 339–347 (2009)

    Article  Google Scholar 

  53. Schwemmer, C.: High-density object removal from x-ray projection images. Master's thesis, Universität Erlangen-N3rnberg (2010)

  54. Blau, Y., Michaeli, T.: The perception-distortion tradeoff. In: Proceedings of Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

  55. Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings of Advances in Neural Information Processing Systems

  56. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of European Conference on Computer Vision

  57. Park, S., Jennings, R., Liu, H., Badano, A., Myers, K.: A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms. Med. Phys. 37, 6253–6270 (2010)

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Dr. Rebecca Fahrig, Stanford University for her great help in obtaining and processing the data.

Funding

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean government (MSIP: Ministry of Science, ICT, and Future Planning) (No. NRF-2020R1A4A1016619, NRF-2020R1F1A1073774), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: KMDF_PR_20200901_0016, 9991006689).

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Correspondence to Jang-Hwan Choi.

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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Choi, D., Kim, W., Lee, J. et al. Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT. Machine Vision and Applications 32, 116 (2021). https://doi.org/10.1007/s00138-021-01240-3

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  • DOI: https://doi.org/10.1007/s00138-021-01240-3

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