An algorithm for noise correction of dual-energy computed tomography material density images

  • Rafael Simon Maia
  • Christian Jacob
  • Amy K. Hara
  • Alvin C. Silva
  • William Pavlicek
  • Mitchell J. Ross
Original Article



Dual-energy computed tomography (DECT) images can undergo a two-material decomposition process which results in two images containing material density information. Material density images obtained by that process result in images with increased pixel noise. Noise reduction in those images is desirable in order to improve image quality.


A noise reduction algorithm for material density images was developed and tested. A three-level wavelet approach combined with the application of an anisotropic diffusion filter was used. During each level, the resulting noise maps are further processed, until the original resolution is reached and the final noise maps obtained. Our method works in image space and, therefore, can be applied to any type of material density images obtained from any DECT vendor. A quantitative evaluation of the noise-reduced images using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and 2D noise power spectrum was done to quantify the improvements.


The noise reduction algorithm was applied to a set of images resulting in images with higher SNR and CNR than the raw density images obtained by the decomposition process. The average improvement in terms of SNR gain was about 49 % while CNR gain was about 52 %. The difference between the raw and filtered regions of interest mean values was far from reaching statistical significance (minimum \(p> 0.89\), average \(p> 0.97\)).


We have demonstrated through a series of quantitative analyses that our novel noise reduction algorithm improves the image quality of DECT material density images.


Material density Dual-energy computed tomography Noise reduction Anisotropic diffusion 


Conflict of interest

Rafael Simon Maia, Christian Jacob, Amy K. Hara, Alvin C. Silva, William Pavlicek and J.Ross Mitchell declare that they have no conflict of interest.

Ethical standards All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008.

Supplementary material

11548_2014_1006_MOESM1_ESM.pdf (7.3 mb)
Supplementary material 1 (pdf 7450 KB)


  1. 1.
    Alvarez R, Seppi E (1979) Comparison of noise and dose in conventional and energy selective computed-tomography. IEEE Trans Nucl Sci 26(2):2853–2855CrossRefGoogle Scholar
  2. 2.
    Balda M, Heismann B, Hornegger J (2010) Value-based noise reduction for low-dose dual-energy computed tomography. In: Springer, Berlin, pp 547–554Google Scholar
  3. 3.
    Borsdorf A, Raupach R, Flohr T, Hornegger J (2008) Wavelet based noise reduction in CT-images using correlation analysis. Med Imaging IEEE Trans 27(12):1685–1703CrossRefGoogle Scholar
  4. 4.
    Hinshaw DA, Dobbins JT III (1995) Recent progress in noise reduction and scatter correction in dual-energy imaging. In: Van Metter RL, Beutel J (eds) Medical imaging. SPIE, pp 134–142Google Scholar
  5. 5.
    Hsieh J (2003) Computed tomography. In: Principles, design, artifacts, and recent advances. Society of photo opticalGoogle Scholar
  6. 6.
    Hsieh J, Thibault JB et al (2010) Methods and apparatus for noise estimation for multi-resolution anisotropic diffusion filtering. US Patent 7,706,497Google Scholar
  7. 7.
    Huda W, Scalzetti EM, Levin G (2000) Technique factors and image quality as functions of patient weight at abdominal CT1. Radiology 217(2):430–435PubMedCrossRefGoogle Scholar
  8. 8.
    Joshi M, Langan D, Sahani D, Kambadakone A, Aluri S, Procknow K, Wu X, Bhotika R, Okerlund D, Kulkarni N, et al. (2010) Effective atomic number accuracy for kidney stone characterization using spectral CT. In: SPIE medical imaging. International society for optics and photonics, pp 76,223K–76,223KGoogle Scholar
  9. 9.
    Kalender W, Klotz E, Kostaridou L (1988) An algorithm for noise suppression in dual energy CT material density images. Med Imaging IEEE Trans 7(3):218–224CrossRefGoogle Scholar
  10. 10.
    Kalender WA (2011) Computed tomography: fundamentals, system technology, image quality, applications. Wiley, New YorkGoogle Scholar
  11. 11.
    Landry G, DeBlois F, Bazalova M, Verhaegen F (2009) MO-D-303A-06: ImaSim, an animated tool for teaching imaging. Med Phys 36(6):2696–2697Google Scholar
  12. 12.
    Landry G, Granton PV, Reniers B, Öllers MC, Beaulieu L, Wildberger JE, Verhaegen F (2011) Simulation study on potential accuracy gains from dual energy ct tissue segmentation for low-energy brachytherapy monte carlo dose calculations. Phys Med Biol 56(19):6257PubMedCrossRefGoogle Scholar
  13. 13.
    Landry G, Reniers B, Granton PV, van Rooijen B, Beaulieu L, Wildberger JE, Verhaegen F (2011) Extracting atomic numbers and electron densities from a dual source dual energy ct scanner: experiments and a simulation model. Radiother Oncol 100(3):375–379PubMedCrossRefGoogle Scholar
  14. 14.
    Li B, Yadava G, Hsieh J (2011) Quantification of head and body CTDIVOL of dual-energy X-ray CT with fast-kVp switching. Med Phys 38(5):2595–2601PubMedCrossRefGoogle Scholar
  15. 15.
    Li X, Chen T (1994) Nonlinear diffusion with multiple edginess thresholds. Pattern Recognit 27(8):1029–1037CrossRefGoogle Scholar
  16. 16.
    Macovski A, Nishimura DG, Doost-Hoseini A, Brody WR (1983) Measurement-dependent filtering: a novel approach to improved SNR. IEEE Trans Med Imaging 2(3):122–127PubMedCrossRefGoogle Scholar
  17. 17.
    Park KK, Oh CH, Akay M (2011) Image enhancement by spectral-error correction for dual-energy computed tomography. In: 2011 33rd Annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 8491–8494Google Scholar
  18. 18.
    Park, KK, Pavlicek W, Boltz T, Paden R, Hara A, Akay M (2009) Image-based dual energy CT improvements using Gram–Schmidt method. In: Samei E, Hsieh J (eds) SPIE medical imaging. SPIE, pp 72,583S–72,583S-9Google Scholar
  19. 19.
    Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639CrossRefGoogle Scholar
  20. 20.
    Petersilka M, Krauss B, Stierstorfer K (2011) Noise reduction in dual-source ct scanning. In: SPIE medical imaging. International society for optics and photonics, pp 79,613Q–79,613QGoogle Scholar
  21. 21.
    Prewitt JM (1970) Picture processing and psychopictorics, chap. Object enhancement and extraction. ElsevierGoogle Scholar
  22. 22.
    Qiang Y (2011) Image denoising based on Haar wavelet transform. In: Electronics and optoelectronics (ICEOE)Google Scholar
  23. 23.
    Johnson TRC, Fink C, Schönberg SO, Reiser MF (eds) (2011) Dual energy CT in clinical practice, vol 201, no 1. Springer, BerlinGoogle Scholar
  24. 24.
    Silva AC, Lawder HJ, Hara A, Kujak J, Pavlicek W (2009) Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm. Am J Roentgenol 194(1):191–199CrossRefGoogle Scholar
  25. 25.
    Tsiotsios C, Petrou M (2013) On the choice of the parameters for anisotropic diffusion in image processing. Pattern Recognit 46(5):1369–1381Google Scholar
  26. 26.
    Warp RJ, Dobbins JT (2003) Quantitative evaluation of noise reduction strategies in dual-energy imaging. Med Phys 30(2):190–198PubMedCrossRefGoogle Scholar
  27. 27.
    Watanabe Y (1999) Derivation of linear attenuation coefficients from ct numbers for low-energy photons. Phys Med Biol 44(9):2201Google Scholar
  28. 28.
    Wu X, Langan DA, Xu D, Benson TM et al (2009) Monochromatic CT image representation via fast switching dual kVp. Proc SPIE 7258(725):845Google Scholar
  29. 29.
    Xu D, Langan DA, Wu X, Pack JD (2010) Method and system for correlated noise suppression in dual energy imaging. US Patent OfficeGoogle Scholar
  30. 30.
    Zbijewski W, Gang G, Wang A, Stayman J, Taguchi K, Carrino J, Siewerdsen J (2013) Noise reduction in material decomposition for low-dose dual-energy cone-beam CT In: SPIE medical imaging. International society for optics and photonics, pp 866,819–866,819Google Scholar

Copyright information

© CARS 2014

Authors and Affiliations

  • Rafael Simon Maia
    • 1
  • Christian Jacob
    • 1
  • Amy K. Hara
    • 2
  • Alvin C. Silva
    • 2
  • William Pavlicek
    • 2
  • Mitchell J. Ross
    • 2
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.Department of RadiologyMayo ClinicScottsdaleUSA

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