A Method for Evaluating the Signal-to-Noise Ratio in Magnetic Resonance Images

Assessment of the signal-to-noise ratio as a control parameter for magnetic resonance imaging (MRI) systems is addressed. Experimental data were used to run a statistical analysis of noise components. Use of a multichannel radio-frequency receiver coil and determination of the coefficient of correction provided the basis of a method for assessing the signal-to-noise ratio of MRI scans.

This is a preview of subscription content, access via your institution.


  1. 1.

    Determination of Signal-to-Noise Ratio (SNR) in Diagnostic Magnetic Resonance Imaging, Standards Publication MS 1-2008 (R2014), National Electrical Manufacturers Association, Rosslyn, VA (2008).

  2. 2.

    Technical Standard for Diagnostic Medical Physics Performance Monitoring of Magnetic Resonance Imaging (MRI) Equipment, American College of Radiology – American Association of Physicists in Medicine (2014); https://www.acr.org/-/media/ACR/Files/Practice-Parameters/MR-Equip.pdf (date accessed: September 19, 2018).

  3. 3.

    Acceptance Testing and Quality Assurance Procedures for Magnetic Resonance Imaging Facilities, Report No. 100 of MR Subcommittee Task Group I, American Association of Physicists in Medicine, College Park, MD (2010).

  4. 4.

    Blinov, N. N. and Snopova, K. A., “Challenges in the passporting and quality control of magnetic resonance tomography systems,” Med. Tekh., 285, No. 3, 34-37 (2014).

    Google Scholar 

  5. 5.

    Zelikman, M. I., Kruchinin, S. A., and Snopova, K. A., “Methods and means of monitoring the operating parameters of magnetic resonance tomographs,” Med. Tekh., 263, No. 5, 27-31 (2010).

    Google Scholar 

  6. 6.

    Sergunova, K. A., et al., “A disk phantom and a method for monitoring the parameters and characteristics of image quality in magnetic resonance angiography,” Biotekhnosfera, 50, No. 2, 2-10 (2017).

    Google Scholar 

  7. 7.

    Aja-Fernández, S. and Vegas-Sánchez-Ferrero, G., Statistical Analysis of Noise in MRI, Springer International Publishing, Switzerland (2016).

    Book  Google Scholar 

  8. 8.

    Petryaikin, A. V. et al., “A dynamic phantom for modeling flows in MR angiography,” Med. Vizual., 21, No. 6, 130-139 (2017).

    Google Scholar 

  9. 9.

    Constantinides, C. D., Atalar, E., and McVeigh, E. R., “Signal-to-noise measurements in magnitude images from NMR phased arrays,” Magn. Reson. Med., 38, No. 5, 852-857 (1997).

    Article  Google Scholar 

  10. 10.

    Yakovleva, T. V., “Review of methods of processing magnetic resonance images and the development of a new two-parameter moments method,” Komp’yut. Issled. Modelir., 6, No. 2, 231-244 (2014).

    Google Scholar 

  11. 11.

    Dietrich O. et al., “Influence of multichannel combination, parallel imaging and other reconstruction techniques on MRI noise characteristics,” Magn. Reson. Imaging, 26, No. 6, 754-762 (2008).

    Article  Google Scholar 

  12. 12.

    Aja-Fernández, S. and Tristán-Vega, A., “A review on statistical noise models for magnetic resonance imaging,” Tech. Report of the LPI; https://www.lpi.tel.uva.es/~santi/personal/docus/noise_survey_tec_report.pdf (date accessed: September 19, 2018).

  13. 13.

    Glagolev, M. V. and Sabrekov, A. F., “Recovery of probability density using histograms in soil science and ecology,” in : The Dynamic Environment and Global Climate Change [in Russian], No. S1, 55-83 (2008).

  14. 14.

    De Azevedo-Marques, P. M. et al., Medical Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy, CRC Press (2017).

Download references

Author information



Corresponding author

Correspondence to K. A. Sergunova.

Additional information

Translated from Meditsinskaya Tekhnika, Vol. 53, No. 3, May-Jun., 2019, pp. 41-43.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sergunova, K.A., Akhmad, E.S. & Potrakhov, N.N. A Method for Evaluating the Signal-to-Noise Ratio in Magnetic Resonance Images. Biomed Eng 53, 207–210 (2019). https://doi.org/10.1007/s10527-019-09910-3

Download citation