Abstract
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.
Similar content being viewed by others
References
A.G. van der Kolk, J. Hendrikse, J.J. Zwanenburg, F. Visser, P.R. Luijten, Eur. J. Radiol. 82, 708–718 (2013)
D.W. McRobbie, E.A. Moore, M.J. Graves, M.R. Prince, MRI from Picture to Proton (Cambridge University Press, Cambridge, 2007)
B.A. Poser, K. Setsompop, NeuroImage 168, 101–118 (2018)
R.J. Stafford, High Field MRI: Technology, Applications, Safety, and Limitations, in 46th AAPM Annual Meeting, American Association of Physicists in Medicine, 2004
A. Deshmane, V. Gulani, M.A. Griswold, N. Seiberlich, J. Magn. Reson. Imaging 36, 55–72 (2012)
I. Ullah, O. Inam, I. Aslam, H. Omer, Appl. Magn. Reson. 50, 243–261 (2019)
M. Qureshi, M. Kaleem, H. Omer, Biomed. Res. 28, 1618–1623 (2017)
D. Xie, L. Bai, Z. Wang, arXiv preprint arXiv:1801.09672 (2018)
K.P. Pruessmann, M. Weiger, M.B. Scheidegger, P. Boesiger, Magn. Reson. Med. 42, 952–962 (1999)
S. Aja-Fernández, G. Vegas-Sánchez-Ferrero, A. Tristán-Vega, Magn. Reson. Imaging 32, 281–290 (2014)
S. Aja-Fernández, C. Alberola-López, C.-F. Westin, IEEE Trans. Image Process. 17, 1383–1398 (2008)
H. Liu, C. Yang, N. Pan, E. Song, R. Green, Magn. Reson. Imaging 28, 1485–1496 (2010)
M. Maggioni, V. Katkovnik, K. Egiazarian, A. Foi, IEEE Trans. Image Process. 22, 119–133 (2013)
D.J. Larkman, R.G. Nunes, Phys. Med. Biol. 52, R15 (2007)
P. Jain, V. Tyagi, Inf. Syst. Front. 18, 159–170 (2016)
L. Sendur, I.W. Selesnick, IEEE Signal Process. Lett. 9, 438–441 (2002)
K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image Process. Algorithms Syst. Neural Netw. Mach. Learn. 6064, 1–12 (2006)
T. Qiu, A. Wang, N. Yu, A. Song, IEEE Trans. Image Process. 22, 80–90 (2013)
S.G. Chang, B. Yu, M. Vetterli, IEEE Trans. Image Process. 9, 1532–1546 (2000)
H. Choi, R.G. Baraniuk, IEEE Signal Process. Lett. 11, 717–720 (2004)
Z. Hou, Pattern Recogn. 36, 1747–1763 (2003)
D.L. Donoho, IEEE Trans. Inf. Theory 41, 613–627 (1995)
M. Blaimer, F. Breuer, M. Mueller, R.M. Heidemann, M.A. Griswold, P.M. Jakob, Top. Magn. Reson. Imaging 15, 223–236 (2004)
A.S. Irfan, A. Nisar, H. Shahzad, H. Omer, Appl. Magn. Reson. 47, 487–498 (2016)
M.A. Ohliger, D.K. Sodickson, NMR Biomed. 19, 300–315 (2006)
J. Mohan, V. Krishnaveni, Y. Guo, Biomed. Signal Process. Control 9, 56–69 (2014)
R.M. Henkelman, Med. Phys. 12, 232–233 (1985)
S.O. Rice, Bell Syst. Tech. J. 23, 282–332 (1944)
O. Dietrich, J.G. Raya, S.B. Reeder, M. Ingrisch, M.F. Reiser, S.O. Schoenberg, Magn. Reson. Imaging 26, 754–762 (2008)
W. Edelstein, P.A. Bottomley, L.M. Pfeifer, Med. Phys. 11, 180–185 (1984)
C.D. Constantinides, E. Atalar, E.R. McVeigh, Magn. Reson. Med. 38, 852–857 (1997)
P. Kellman, E.R. McVeigh, Magn. Reson. Med. 54, 1439–1447 (2005)
R.D. Da Silva, R. Minetto, W.R. Schwartz, H. Pedrini, Pattern Anal. Appl. 16, 567–580 (2013)
P. Jain, V. Tyagi, Multimed. Tools Appl. 76, 1659–1679 (2017)
S.G. Mallat, IEEE Trans. Pattern Anal. Mach. Intell. 7, 674–693 (1989)
M. Dai, C. Peng, A.K. Chan, D. Loguinov, IEEE Trans. Geosci. Remote Sens. 42, 1642–1648 (2004)
A.-J. Van Der Veen, E.F. Deprettere, A.L. Swindlehurst, Proc. IEEE 81, 1277–1308 (1993)
D. Fish, J. Grochmalicki, E. Pike, JOSA A 13, 464–469 (1996)
K. Konstantinides, B. Natarajan, G.S. Yovanof, IEEE Trans. Image Process. 6, 479–483 (1997)
Y. Wongsawat, K.R. Rao, S. Oraintara, Multichannel SVD-based image de-noising, in 2005 IEEE International Symposium on Circuits and Systems, (2005), pp. 5990–5993
R. Frayne, B.G. Goodyear, P. Dickhoff, M.L. Lauzon, R.J. Sevick, Investig. Radiol. 38, 385–402 (2003)
O. Inam, M. Qureshi, S.A. Malik, H. Omer, BioMed. Res. Int. 2017, 3872783 (2017)
J.H. Letcher, Magn. Reson. Imaging 7, 581–583 (1989)
G.P. Nason, B.W. Silverman, in The Stationary Wavelet Transform and Some Statistical Applications. Wavelets and Statistics (Springer, New York, 1995), pp. 281–299
Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, IEEE Trans. Image Process. 13, 600–612 (2004)
S.E. Ghrare, S.M. Shreef, World Acad. Sci. Eng. Technol. 72, 12 (2012)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Inam, O., Qureshi, M. & Omer, H. De-noising Multi-coil Magnetic Resonance Imaging Using Patch-Based Adaptive Filtering in Wavelet Domain. Appl Magn Reson 50, 1325–1343 (2019). https://doi.org/10.1007/s00723-019-01153-5
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00723-019-01153-5