High-quality X-ray computed tomography reconstruction using projected and interpolated images

High-quality X-ray computed tomography reconstruction using projected and interpolated images

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Recent advances in computation power have allowed computed tomography (CT) to utilise iterative reconstruction (IR) algorithms. The IR technique can handle noisy data and reconstructs optimal CT images from limited projected images. As of cyclic image processing, IR improves the quality of CT images. This approach requires a minimum number of projections to reconstruct an image; however, decreasing the number of projections to 90 can create artefacts and degrade reconstruction quality. To overcome this limitation, the optical flow technique can compute flow vectors between two consecutive projections to generate projected images between frames. Here, optical flow-based frame interpolation combined with the ordered subset-modified iterative technique is proposed to reduce computation time, lower the number of projections, and increase reconstruction quality of CT images. The proposed technique can be used to reconstruct a CT image from 90 projections at 4 degree intervals between projection sequences. This approach produces a much better quality reconstruction compared to that produced by an analytical algorithm, which uses 360 projections. The inclusion of an ordered subset reconstructs CT images quickly by accelerating streaming architecture.


    1. 1)
      • 1. Brooks, R.A., Di Chiro, G.: ‘Principles of computer assisted tomography (CAT) in radiographic and radioisotopic imaging’, Phys. Med. Biol., 1976, 21, (5), p. 689.
    2. 2)
      • 2. Hounsfield, G.N.: ‘Computerized transverse axial scanning (tomography): part 1. Description of system’, Br. J. Radiol., 1973, 46, (552), pp. 10161022.
    3. 3)
      • 3. Fleischmann, D., Boas, F.E.: ‘Computed tomograph–old ideas and new technology’, Eur. Radiol., 2011, 21, (3), pp. 510517.
    4. 4)
      • 4. Feldkamp, L., Davis, L., Kress, J.: ‘Practical cone-beam algorithm’, J. Opt. Soc. Am. A, 1984, 1, (6), pp. 612619.
    5. 5)
      • 5. Ouyang, L., Solberg, T., Wang, J.: ‘Effects of the penalty on the penalized weighted least-squares image reconstruction for low-dose CBCT’, Phys. Med. Biol., 2011, 56, (17), pp. 55355552.
    6. 6)
      • 6. Choi, K., Wang, J., Zhu, L., et al: ‘Compressed sensing based cone-beam computed tomography reconstruction with a first-order method’, Med. Phys., 2010, 37, (9), pp. 51135125.
    7. 7)
      • 7. Lee, H., Xing, L., Davidi, R., et al: ‘Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints’, Phys. Med. Biol., 2012, 57, (8), pp. 22872307.
    8. 8)
      • 8. Lefkimmiatis, S., Bourquard, A., Unser, M.: ‘Hessian-based regularization for 3-D microscopy image restoration’. IEEE Int. Symp. on Biomedical Imaging, 2012, pp. 17311734.
    9. 9)
      • 9. Horn, B.K., Schunck, B.G.: ‘Determining optical flow’, Artif. Intell., 1981, 17, (1–3), pp. 185203.
    10. 10)
      • 10. Lucas, B.D., Kanade, T.: ‘An iterative image registration technique with an application to stereo vision’, IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence, 1981, 2, pp. 674679.
    11. 11)
      • 11. Deriche, R., Kornprobst, P., Aubert, G.: ‘Optical-flow estimation while preserving its discontinuities: a variational approach’. Asian Conf. on Computer Vision, 1995.
    12. 12)
      • 12. Memin, E., Perez, P.: ‘A multigrid approach for hierarchical motion estimation’. Sixth Int. Conf. on Computer Vision, 1998.
    13. 13)
      • 13. Nagel, H.-H., Enkelmann, W.: ‘An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences’, IEEE Trans. Pattern Anal. Mach. Intell., 1986, 8, (5), pp. 565593.
    14. 14)
      • 14. Ehrhardt, J., Werner, R., Saring, D., et al: ‘An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing’, Med. Phys., 2007, 34, (2), pp. 711721.
    15. 15)
      • 15. Leng, J., Xu, G., Zhang, Y.: ‘Medical image interpolation based on multi-resolution registration’, Comput. Math. Appl., 2013, 66, (1), pp. 118.
    16. 16)
      • 16. Andersen, A..: ‘Algebraic reconstruction in CT from limited views’, IEEE Trans. Med. Imaging, 1989, 8, pp. 5055.
    17. 17)
      • 17. Xu, F., Mueller, K., Jones, M., et al: ‘On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on GPUs’. MICCAI (Workshop on High-performance Medical Image Computing & Computer Aided Intervention), New York, September 2008.
    18. 18)
      • 18. Bappy, D.M., Jeon, I.: ‘Modified simultaneous iterative reconstruction technique for fast, high-quality CT reconstruction’, IET Image Process., 2017, 11, (9), pp. 7017089, doi: 10.1049/iet-ipr.2017.0304.
    19. 19)
      • 19. Lu, Y., Wang, W., Chen, S., et al: ‘Accelerating algebraic reconstruction using CUDA-enabled GPU’. Sixth International Conference on Computer Graphics, Imaging and Visualization, 2009, pp. 480485.
    20. 20)
      • 20. Sun, D., Roth, S., Black, M.J.: ‘Secrets of optical flow estimation and their principles’. Computer Vision and Pattern Recognition (CVPR), 2010.
    21. 21)
      • 21. Oliveira, N., Mota, A.M., Matela, N., et al: ‘Dynamic relaxation in algebraic reconstruction technique (ART) for breast tomosynthesis imaging’, Comput. Methods Programs Biomed., 2016, 132, pp. 189196.
    22. 22)
      • 22. Censor, Y., Elfving, T.: ‘Block-iterative algorithms with diagonally scaled oblique projections for the linear feasibility problem’, SIAM J. Matrix Anal. Appl., 2002, 24, pp. 4058.
    23. 23)
      • 23. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600612.
    24. 24)
      • 24. Abdou, I.E., Pratt, W.K.: ‘Quantitative design and evaluation of enhancement/thresholding edge detectors’, Proc. IEEE, 1979, 67, (5), pp. 753763.

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