View Aliasing Artifacts Reduction Method Based on 4D Cone-Beam CT Reconstruction with Joint Projection Data

Shaohua Zhi, Xuanqin Mou


Published in:Fully3D 2017 Proceedings


Cone-Beam CT, 4D-CBCT reconstruction, MKB algorithm, joint projection data
Although the quality of the phase-resolved reconstruction images could be improved by the four-dimensional cone-beam computed tomography (4D-CBCT) by reducing the motion blurring artifacts, it may still be degraded by severe viewaliasing artifacts because of highly under-sampled projections at each phase. Inspired by the strong correlation between different phase-resolved reconstructed images, we present a simple and effective approach to estimate a set of full-sampled projections for every individual respiratory phase and then to incorporate them into the 4D-CBCT iterative reconstruction scheme. In the implementation of the 4D-CBCT iterative reconstruction scheme,a coupled distance-driven forward and backward projection operator via GPU is introduced. The proposed method has been tested in a digital XCAT phantom and a clinical patient case. Quantitative evaluations indicate that a 15.7% and 9.9%decrease in the root-mean-square error (RMSE) are achieved by our method when comparing with the conventional 4D-CBCT reconstruction method and the classic McKinnon/Bates algorithm (MKB), respectively. At the same time, our method is also valid by calculating the contrast-to-noise ratio (CNR) of a region of interest (ROI). The result shows that the CNR of our method is 1.34, which is better than that of the MKB algorithm.
Shaohua Zhi
Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong University, China
Xuanqin Mou
Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong University, China
  1. D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, and A. A. Martinez, “Flatpanel cone-beam computed tomography for image-guided radiation therapy,” International Journal of Radiation Oncology*Biology*Physics,vol. 53, no. 5, pp. 1337 – 1349, 2002
  2. J.-J. Sonke, L. Zijp, P. Remeijer, and M. van Herk, “Respiratory correlated cone beam ct,” Medical Physics, vol. 32, no. 4, pp. 1176–1186, 2005.
  3. M. Brehm, P. Paysan, M. Oelhafen, P. Kunz, and M. Kachelrie, “Selfadapting cyclic registration for motion-compensated cone-beam ct in image-guided radiation therapy,” Medical Physics, vol. 39, no. 12, pp.7603–7618, 2012.
  4. J. Wang and X. Gu, “High-quality four-dimensional cone-beam ct by deforming prior images,” Physics in Medicine and Biology, vol. 58,no. 2, p. 231, 2013.
  5. G.-H. Chen, J. Tang, and S. Leng, “Prior image constrained compressed sensing (piccs): A method to accurately reconstruct dynamic ct images from highly undersampled projection data sets,” Medical Physics, vol. 35, no. 2, pp. 660–663, 2008.
  6. X. Jia, Z. Tian, Y. Lou, J.-J. Sonke, and S. B. Jiang, “Four-dimensional cone beam ct reconstruction and enhancement using a temporal nonlocal means method,” Medical Physics, vol. 39, no. 9, pp. 5592–5602, 2012.
  7. H. Gao, J.-F. Cai, Z. Shen, and H. Zhao, “Robust principal component analysis-based four-dimensional computed tomography,” Physics in Medicine and Biology, vol. 56, no. 11, p. 3181, 2011.
  8. K. L. Garden and R. A. Robb, “3-d reconstruction of the heart from few projections: A practical implementation of the mckinnon-bates algorithm,” IEEE Transactions on Medical Imaging, vol. 5, no. 4, pp.233–239, Dec 1986.
  9. S. Leng, J. Zambelli, R. Tolakanahalli, B. Nett, P. Munro, J. Star-Lack, B.Paliwal, and G.-H. Chen, “Streaking artifacts reduction in four-dimensional cone-beam computed tomography,” Medical Physics,vol. 35, no. 10, pp. 4649–4659, 2008.
  10. Z. Zheng, M. Sun, J. Pavkovich, and J. Star-Lack, “Fast 4d conebeam reconstruction using the mckinnon-bates algorithm with truncation correction and nonlinear filtering,” in Proc. SPIE 7961, Medical Imaging 2011: Physics of Medical Imaging, N. J. Pelc, E. Samei, and R. M.Nishikawa, Eds., vol. 7961, March 2011, pp. 79 612U–79 612U–8.
  11. B. D. Man and S. Basu, “Distance-driven projection and backprojection in three dimensions,” Physics in Medicine and Biology, vol. 49, no. 11,p. 2463, 2004.
  12. G. T. Herman, Fundamentals of computerized tomography: image reconstruction from projections. Springer Science & Business Media,2009.
  13. L. Singaravelu, C. Pu, H. Hrtig, and C. Helmuth, “Reducing TCB Complexity for Security-sensitive Applications: Three Case Studies,”in Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006, ser. EuroSys ’06. New York, NY, USA:ACM, 2006, pp. 161–174.
  14. F. Arcadu, M. Stampanoni, and F. Marone, “On the crucial impact of the coupling projector-backprojector in iterative tomographic reconstruction,”arXivpreprint arXiv:1612.05515, 2016.
  15. T. M. Peters, “Algorithms for fast back- and re-projection in computed tomography,” IEEE Transactions on Nuclear Science, vol. 28, no. 4, pp. 3641–3647, Aug 1981.
  16. W. Zhuang, S. S. Gopal, and T. J. Hebert, “Numerical evaluation of methods for computing tomographic projections,” IEEE Transactions on Nuclear Science, vol. 41, no. 4, pp. 1660–1665, Aug 1994.
  17. W. P. Segars, G. Sturgeon, S. Mendonca, J. Grimes, and B. M. W. Tsui,“4d xcat phantom for multimodality imaging research,” Medical Physics,vol. 37, no. 9, pp. 4902–4915, 2010.
  18. J. Sonke, M. V. Herk, J. Belderbos, M. Rossi, A. Betgen, and J. Lebesque, “An off-line 4d cone beam ct based correction protocol for lung tumor motion,”International Journal of Radiation Oncology*Biology*Physics, vol. 63, pp. S389–S390, 2005