De-noising Multi-coil Magnetic Resonance Imaging Using Patch-Based Adaptive Filtering in Wavelet Domain

  • Omair InamEmail author
  • Mahmood Qureshi
  • Hammad Omer
Original Paper


Magnetic resonance imaging (MRI) frequently requires transform domain de-noising methods to preserve important features in the reconstructed images such as corners, sharp structures, and edges. Wavelet transform-based image de-noising is a standard approach used in MRI to recover smooth surface and sharp edges from the given noisy MR images, thereby improving diagnostic interpretations. Parallel magnetic resonance imaging (pMRI) techniques such as SENSE have been recently developed with an aim to improve the data acquisition speed, signal-to-noise ratio (SNR), and spatial resolution of the reconstructed images. However, the SENSE reconstruction algorithm often encounters noise during data acquisition and reconstruction process which not only contaminates the quality of the reconstructed images but also leads to poor diagnostic interpretations in clinical settings. During SENSE reconstruction process, noise can appear in the reconstructed image mainly due to two reasons (1) imperfections in the receiver coils; (2) un-folding the aliased images of multiple receiver coils to obtain a single composite image. In this paper, a new adaptive patch-based filtering in wavelet domain is presented to recover sharp structures and edges without introducing any artifacts in the SENSE reconstructed images. The proposed method uses soft-thresholding function as a shrinkage process which typically involves thresholding the small wavelet coefficients to reduce the noise without affecting the important features in the SENSE reconstructed images. For the evaluation of the proposed method, several experiments are performed using simulated phantom and in vivo data sets. The SENSE reconstruction quality using the proposed method is compared with contemporary de-nosing techniques, in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). Experimental results demonstrate that the SENSE reconstruction using the proposed method when compared to the other contemporary de-nosing methods successfully removes the noise and preserves the fine details in the reconstructed MR images without introducing blurring artifacts.


Supplementary material

723_2019_1153_MOESM1_ESM.docx (17 kb)
Supplementary material 1 (DOCX 16 kb)


  1. 1.
    A.G. van der Kolk, J. Hendrikse, J.J. Zwanenburg, F. Visser, P.R. Luijten, Eur. J. Radiol. 82, 708–718 (2013)CrossRefGoogle Scholar
  2. 2.
    D.W. McRobbie, E.A. Moore, M.J. Graves, M.R. Prince, MRI from Picture to Proton (Cambridge University Press, Cambridge, 2007)Google Scholar
  3. 3.
    B.A. Poser, K. Setsompop, NeuroImage 168, 101–118 (2018)CrossRefGoogle Scholar
  4. 4.
    R.J. Stafford, High Field MRI: Technology, Applications, Safety, and Limitations, in 46th AAPM Annual Meeting, American Association of Physicists in Medicine, 2004Google Scholar
  5. 5.
    A. Deshmane, V. Gulani, M.A. Griswold, N. Seiberlich, J. Magn. Reson. Imaging 36, 55–72 (2012)CrossRefGoogle Scholar
  6. 6.
    I. Ullah, O. Inam, I. Aslam, H. Omer, Appl. Magn. Reson. 50, 243–261 (2019)CrossRefGoogle Scholar
  7. 7.
    M. Qureshi, M. Kaleem, H. Omer, Biomed. Res. 28, 1618–1623 (2017)Google Scholar
  8. 8.
    D. Xie, L. Bai, Z. Wang, arXiv preprint arXiv:1801.09672 (2018)
  9. 9.
    K.P. Pruessmann, M. Weiger, M.B. Scheidegger, P. Boesiger, Magn. Reson. Med. 42, 952–962 (1999)CrossRefGoogle Scholar
  10. 10.
    S. Aja-Fernández, G. Vegas-Sánchez-Ferrero, A. Tristán-Vega, Magn. Reson. Imaging 32, 281–290 (2014)CrossRefGoogle Scholar
  11. 11.
    S. Aja-Fernández, C. Alberola-López, C.-F. Westin, IEEE Trans. Image Process. 17, 1383–1398 (2008)ADSMathSciNetCrossRefGoogle Scholar
  12. 12.
    H. Liu, C. Yang, N. Pan, E. Song, R. Green, Magn. Reson. Imaging 28, 1485–1496 (2010)CrossRefGoogle Scholar
  13. 13.
    M. Maggioni, V. Katkovnik, K. Egiazarian, A. Foi, IEEE Trans. Image Process. 22, 119–133 (2013)ADSMathSciNetCrossRefGoogle Scholar
  14. 14.
    D.J. Larkman, R.G. Nunes, Phys. Med. Biol. 52, R15 (2007)ADSCrossRefGoogle Scholar
  15. 15.
    P. Jain, V. Tyagi, Inf. Syst. Front. 18, 159–170 (2016)CrossRefGoogle Scholar
  16. 16.
    L. Sendur, I.W. Selesnick, IEEE Signal Process. Lett. 9, 438–441 (2002)ADSCrossRefGoogle Scholar
  17. 17.
    K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image Process. Algorithms Syst. Neural Netw. Mach. Learn. 6064, 1–12 (2006)Google Scholar
  18. 18.
    T. Qiu, A. Wang, N. Yu, A. Song, IEEE Trans. Image Process. 22, 80–90 (2013)ADSMathSciNetCrossRefGoogle Scholar
  19. 19.
    S.G. Chang, B. Yu, M. Vetterli, IEEE Trans. Image Process. 9, 1532–1546 (2000)ADSMathSciNetCrossRefGoogle Scholar
  20. 20.
    H. Choi, R.G. Baraniuk, IEEE Signal Process. Lett. 11, 717–720 (2004)ADSCrossRefGoogle Scholar
  21. 21.
    Z. Hou, Pattern Recogn. 36, 1747–1763 (2003)CrossRefGoogle Scholar
  22. 22.
    D.L. Donoho, IEEE Trans. Inf. Theory 41, 613–627 (1995)CrossRefGoogle Scholar
  23. 23.
    M. Blaimer, F. Breuer, M. Mueller, R.M. Heidemann, M.A. Griswold, P.M. Jakob,  Top. Magn. Reson. Imaging 15, 223–236 (2004)CrossRefGoogle Scholar
  24. 24.
    A.S. Irfan, A. Nisar, H. Shahzad, H. Omer, Appl. Magn. Reson. 47, 487–498 (2016)CrossRefGoogle Scholar
  25. 25.
    M.A. Ohliger, D.K. Sodickson, NMR Biomed. 19, 300–315 (2006)CrossRefGoogle Scholar
  26. 26.
    J. Mohan, V. Krishnaveni, Y. Guo, Biomed. Signal Process. Control 9, 56–69 (2014)CrossRefGoogle Scholar
  27. 27.
    R.M. Henkelman, Med. Phys. 12, 232–233 (1985)CrossRefGoogle Scholar
  28. 28.
    S.O. Rice, Bell Syst. Tech. J. 23, 282–332 (1944)ADSCrossRefGoogle Scholar
  29. 29.
    O. Dietrich, J.G. Raya, S.B. Reeder, M. Ingrisch, M.F. Reiser, S.O. Schoenberg, Magn. Reson. Imaging 26, 754–762 (2008)CrossRefGoogle Scholar
  30. 30.
    W. Edelstein, P.A. Bottomley, L.M. Pfeifer, Med. Phys. 11, 180–185 (1984)CrossRefGoogle Scholar
  31. 31.
    C.D. Constantinides, E. Atalar, E.R. McVeigh, Magn. Reson. Med. 38, 852–857 (1997)CrossRefGoogle Scholar
  32. 32.
    P. Kellman, E.R. McVeigh, Magn. Reson. Med. 54, 1439–1447 (2005)CrossRefGoogle Scholar
  33. 33.
    R.D. Da Silva, R. Minetto, W.R. Schwartz, H. Pedrini, Pattern Anal. Appl. 16, 567–580 (2013)CrossRefGoogle Scholar
  34. 34.
    P. Jain, V. Tyagi, Multimed. Tools Appl. 76, 1659–1679 (2017)CrossRefGoogle Scholar
  35. 35.
    S.G. Mallat, IEEE Trans. Pattern Anal. Mach. Intell. 7, 674–693 (1989)ADSCrossRefGoogle Scholar
  36. 36.
    M. Dai, C. Peng, A.K. Chan, D. Loguinov, IEEE Trans. Geosci. Remote Sens. 42, 1642–1648 (2004)ADSCrossRefGoogle Scholar
  37. 37.
    A.-J. Van Der Veen, E.F. Deprettere, A.L. Swindlehurst, Proc. IEEE 81, 1277–1308 (1993)CrossRefGoogle Scholar
  38. 38.
    D. Fish, J. Grochmalicki, E. Pike, JOSA A 13, 464–469 (1996)ADSCrossRefGoogle Scholar
  39. 39.
    K. Konstantinides, B. Natarajan, G.S. Yovanof, IEEE Trans. Image Process. 6, 479–483 (1997)ADSCrossRefGoogle Scholar
  40. 40.
    Y. Wongsawat, K.R. Rao, S. Oraintara, Multichannel SVD-based image de-noising, in 2005 IEEE International Symposium on Circuits and Systems, (2005), pp. 5990–5993Google Scholar
  41. 41.
    R. Frayne, B.G. Goodyear, P. Dickhoff, M.L. Lauzon, R.J. Sevick, Investig. Radiol. 38, 385–402 (2003)Google Scholar
  42. 42.
    O. Inam, M. Qureshi, S.A. Malik, H. Omer, BioMed. Res. Int. 2017, 3872783 (2017)CrossRefGoogle Scholar
  43. 43.
    J.H. Letcher, Magn. Reson. Imaging 7, 581–583 (1989)CrossRefGoogle Scholar
  44. 44.
    G.P. Nason, B.W. Silverman, in The Stationary Wavelet Transform and Some Statistical Applications. Wavelets and Statistics (Springer, New York, 1995), pp. 281–299zbMATHGoogle Scholar
  45. 45.
    Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, IEEE Trans. Image Process. 13, 600–612 (2004)ADSCrossRefGoogle Scholar
  46. 46.
    S.E. Ghrare, S.M. Shreef, World Acad. Sci. Eng. Technol. 72, 12 (2012)Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringCOMSATS University IslamabadIslamabadPakistan

Personalised recommendations