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Aliased noise in X-ray CT images and band-limiting processing as a preventive measure

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Abstract

X-ray CT projection data often include components with frequencies that are markedly higher than the pixel Nyquist frequency f PN, which is determined by the pixel size. Noise components higher than f PN are folded back into a region lower than f PN through the backprojection process, thereby creating aliased noise. With clinical CT scanners, we evaluated the aliased noise using an aliasing prevention measure, band-limiting processing (BLP), which suppresses frequency components higher than f PN in the projection data. Indices we used to evaluate improvement by BLP were the noise power spectrum (NPS), modulation transfer function (MTF), signal-to-noise-ratio (SNR) spectrum, matched filter SNR (MF SNR), and two-alternative forced-choice (2-AFC) test. With BLP, the NPS was decreased not only beyond f PN, but also within f PN. The same level of MTF was maintained as that without BLP within f PN. No remarkable reduction in spatial resolution was observed. The SNR spectrum and the MF SNR of the BLP image nearly agreed with those of an ideal state without aliased noise. A notable improvement in the visuoperceptual image quality by BLP was recognized with a reconstruction field of view (FOV) of more than 45 cm. We then applied BLP to clinical data and confirmed that significant aliased noise of a large FOV image was removed without notable side effects. The results showed that at least some CTs suffering from aliased noise can be improved by proper band-limiting.

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References

  1. Kijewski MF, Judy PF. The noise power spectrum of CT images. Phys Med Biol. 1987;32:565–75.

    Article  CAS  PubMed  Google Scholar 

  2. Vaz MS, Mersereau R. On Filter Design for CT Reconstruction. In: Proceedings of Tenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. 2009. pp. 154–57.

  3. Archibald R, Gelb A. A method to reduce the Gibbs ringing artifact in MRI scans while keeping tissue boundary integrity. IEEE Trans Med Imaging. 2002;21:305–19.

    Article  PubMed  Google Scholar 

  4. Jiang H. Computed Tomography: Principles, Design, Artifacts, and Recent Advances, Second Edition. SPIE Press; 2009. p. 83.

  5. Sato K, Sakamoto H, Goto M, Osanai M, Machida Y, et al. A Method of CT Simulation for Users by Using Projection Data Processing. Bull Sch Health Sci Tohoku Univ. 2011;20:91–102 (in Japanese).

    Google Scholar 

  6. Boedeker KL, Cooper VN, McNitt-Gray MF. Application of the noise power spectrum in modern diagnostic MDCT: part II. Noise power spectra and signal-to-noise. Phys Med Biol. 2007;52:4047–61.

    Article  CAS  PubMed  Google Scholar 

  7. Goto M, Sato K, Minakuchi S, Miura T, Osanai M, et al. Noise Analysis of CT Image: improvement in Low-Frequency Area. Bull Sch Health Sci Tohoku Univ. 2011;20:55–61 (in Japanese).

    Google Scholar 

  8. Jiang H, Chen WR, Liu H. Effect of window function on noise power spectrum measurements in digital X-ray imaging. Proc SPIE. 2002;4615:91–7.

    Article  Google Scholar 

  9. Boedeker KL, Cooper VN, McNitt-Gray MF. Application of the noise power spectrum in modern diagnostic MDCT: part I. Measurement of noise power spectra and noise equivalent quanta. Phys Med Biol. 2007;52:4027–46.

    Article  CAS  PubMed  Google Scholar 

  10. Mori I, Machida Y. Deriving the modulation transfer function of CT from extremely noisy edge profiles. Radiol Phys Technol. 2009;2:22–32.

    Article  PubMed  Google Scholar 

  11. Richard S, Husarik DB, Yadava G, Murphy SN, Samei E. Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms. Med Phys. 2012;39:4115–22.

    Article  PubMed  Google Scholar 

  12. Ohisa T, Ito M, Sasaki S, Abiko S, Sasaki T, et al. Evaluation of CT-Image (III)—Intercomparison among Different CT Equipment. Bull Coll Med Sci Tohoku Univ. 1995;4:45–52 (in Japanese).

    Google Scholar 

  13. Hara T, Ichikawa K, Sanada S, Ida Y. Image quality dependence on in-plane positions and directions for MDCT images. Eur J Radiol. 2010;75:114–21.

    Article  PubMed  Google Scholar 

  14. Ichikawa K, Hara T, Niwa S, Yamaguchi I, Ohashi K. Evaluation of low contrast detectability using signal-to-noise ratio in computed tomography. Med Imaging Inf Sci. 2007;24:106–11 (in Japanese).

    Google Scholar 

  15. Loo LN, Doi K, Metz CE. A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads. Phys Med Biol. 1984;7:837–56.

    Article  Google Scholar 

  16. Richard S, Siewerdsen JH. Comparison of model and human observer performance for detection and discrimination tasks using dual-energy X-ray images. Med Phys. 2008;35:5043–53.

    Article  PubMed Central  PubMed  Google Scholar 

  17. Tward DJ, Siewerdsen JH, Daly MJ, Richard S, Moseley DJ, Jaffray DA, et al. Soft-tissue detectability in cone-beam CT: evaluation by 2AFC tests in relation to physical performance metrics. Med Phys. 2007;34:4459–71.

    Article  CAS  PubMed  Google Scholar 

  18. Herrmann C, Buhr E, Hoeschen D, Fan SY. Comparison of ROC and AFC methods in a visual detection task. Med Phys. 1993;20:805–11.

    Article  CAS  PubMed  Google Scholar 

  19. Tapiovaaraa MJ, SandBorg M. How should low-contrast detail detectability be measured in fluoroscopy? Med Phys. 2004;31:2564–76.

    Article  Google Scholar 

  20. Reiser I, Nishikawa MR. Identification of simulated microcalcifications in white noise and mammographic backgrounds. Med Phys. 2006;33:2905–11.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 21591591.

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The authors declare that they have no conflict of interest.

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Correspondence to Kazuhiro Sato.

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Sato, K., Shidahara, M., Goto, M. et al. Aliased noise in X-ray CT images and band-limiting processing as a preventive measure. Radiol Phys Technol 8, 178–192 (2015). https://doi.org/10.1007/s12194-015-0306-5

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  • DOI: https://doi.org/10.1007/s12194-015-0306-5

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