Hybrid Iterative Reconstruction for Low Radiation Dose Computed Tomography

  • Jinhua ShengEmail author
  • Bin Chen
  • Bocheng Wang
  • Qingqiang Liu
  • Yangjie Ma
  • Weixiang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


The purpose of this paper is to study the use of hybrid iterative reconstruction (HIR) technique for radiation dose reduction and its effect on low-contrast resolution. This method is designed to create prior information for improving image quality from low dose CT scanners. We compare the performance of lower radiation dose with the HIR and standard dose with the filtered back projection (FBP) using catphan®504 phantom, which is used to measure various image quality parameters. Results show that there are continuous linear reduction of noise and linear increase of CNR with increasing HIR levels compared to FBP for any given scanning protocol. It is possible to provide equivalent diagnostic image quality at low dose. In this paper, we use a quantitative method to evaluate the noise characteristics. Evidence from phantom tests demonstrates that the shape of NPSHIR is shifted continuously to low frequency with increasing HIR levels compared to FBP for any given scanning protocol. Our study confirms that even if there are continuous reduction of noise and increase of CNR with increasing HIR levels, the performance of human observers did not seem to be improved simultaneously because coarser noise could appear. Our finding that the low-frequency components (HIR) are greater than one of FBP (previously believed) may result in the discrepancy between the performance of human observers and that of the ideal low-contrast objects.


Computed tomography Low contrast Noise power spectrum HIR Image quality Iterative reconstruction 


  1. 1.
    Hounsfield, G.N.: Computerized transverse axial scanning (tomography). Part 1. Description of system. Br. J. Radiol. 46, 1016–1022 (1973)CrossRefGoogle Scholar
  2. 2.
    Hara, A.K., Paden, R.G., Silva, A.C., Kujak, K.L., Lawder, H.J., Pavlicek, W.: Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study. AJR 193, 764–771 (2009)CrossRefGoogle Scholar
  3. 3.
    Nuyts, J., De Man, B., Dupont, P., Defrise, M., Suetens, P., Mortelmans, L.: Iterative reconstruction for helical CT: a simulation study. Phys. Med. Biol. 43, 729–737 (1998)CrossRefGoogle Scholar
  4. 4.
    Liu, Y.J., Zhu, P.P., Chen, B., et al.: A new iterative algorithm to reconstruct the refractive index. Phys. Med. Biol. 52, L5–L13 (2007)CrossRefGoogle Scholar
  5. 5.
    Cheng, L., Fang, T., Tyan, J.: Fast iterative adaptive reconstruction in low-dose CT imaging. In: Proceedings of the IEEE International Conference on Image Processing, pp. 889–892. IEEE, New York, NY (2006)Google Scholar
  6. 6.
    Casey, B., Keen, C.: Philips Touts MRI Advances, CT dose reduction at RSNA. RSNA, Oak Brook (2009)Google Scholar
  7. 7.
    Noël, P.B., Fingerle, A.A., Renger, B., et al.: A clinical comparison study of a novel statistical iterative and filtered backprojection reconstruction. In: Physics of Medical Imaging Proceedings of SPIE, vol. 7961 (2011)Google Scholar
  8. 8.
    Division, Siemens Healthcare Imaging: Mathematical Approach Contributes to Lower Radiation Dose in Computed Tomography: Siemens Develops Innovative Method for Iterative Reconstruction of CT Images. Siemens, Erlangen (2009)Google Scholar
  9. 9.
    Bruder, H., Raupach, R., Sedlmair, M., Sunnegardh, J., Stierstorfer, K., Flohr, T.G.: Reduction of radiation dose in CT with an FBP-based iterative reconstruction technique (abstract). B-568, insight into imaging (ECR abstract book). S131 (2010)Google Scholar
  10. 10.
    Joemai, R.: Improved image quality in clinical CT by AIDR. Toshiba Med. Syst. J. Vis. 16, 1–3 (2010)Google Scholar
  11. 11.
    Jensen, K., Catrine, A., Martinsen, T., Tingberg, A., et al.: Comparing five different iterative reconstruction algorithms for computed tomography in an ROC study. Eur. Radiol. 24, 2989–3002 (2014)CrossRefGoogle Scholar
  12. 12.
    Hsieh, J.: Computed Tomography Principles, Design, Artifacts, and Recent Advances, vol. 2. SPIE Press, Bellingham (2009)Google Scholar
  13. 13.
    Catphan@504 Phantom Manual (The phantom Laboratory, Salem, New York).
  14. 14.
    Benítez, R.B., Ning, R., Conover, D., Liu, S.H.: NPS characterization and evaluation of a cone beam CT breast imaging system. J. X-Ray Sci. Technol. 17, 17–40 (2009)Google Scholar
  15. 15.
    Gupta, A.K., Nelson, R.C., Johnson, G.A., Paulson, E.K., Delong, D.M., Yoshizumi, T.T.: Optimization of eight-element multi-detector row helical CT technology for evaluation of the abdomen. Radiology 227, 239–745 (2003)CrossRefGoogle Scholar
  16. 16.
    Hanson, K.M.: Detectability in computed tomographic images. Med. Phys. 6, 441–451 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jinhua Sheng
    • 1
    Email author
  • Bin Chen
    • 1
  • Bocheng Wang
    • 1
    • 2
  • Qingqiang Liu
    • 1
  • Yangjie Ma
    • 1
  • Weixiang Liu
    • 1
  1. 1.College of Computer ScienceHangzhou Dianzi UniversityHangzhouChina
  2. 2.Zhejiang University of Media and CommunicationHangzhouChina

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