Magnetic Resonance Image Averaging with Local Offset Correction

doi:10.11938/cjmr20162525

Abstract

Abstract: In magnetic resonance imaging (MRI), data averaging is often used to improve signal-to-noise ratio (SNR) of the images. However, image blurring can be induced by averaging if movements occur during scanning. Inspired by the patch-matching method used in the non-local means algorithm, a new method to find out local offsets of structures in multiple images was proposed by comparing the neighborhood similarities of the image patches. The local offsets could then be corrected before weighted averaging of the images. The performance of the proposed method was verified with both phantom and patient images. The results demonstrated that the proposed algorithm could improve SNR while preserving the image edges and details correctly.

Key words: non-local means, image denoising, averaging, magnetic resonance imaging (MRI)

CLC Number:

O482.53

LI Wen-jing, XIE Hai-bin, YAN Xu, ZHOU Min-xiong, XIANG Zhi-ming, YANG Guang. Magnetic Resonance Image Averaging with Local Offset Correction[J]. Chinese Journal of Magnetic Resonance, 2017, 34(3): 294-301.

References

[1] MOHAN J, KRISHNAVENI V, GUO Y. A survey on the magnetic resonance image denoising methods[J]. Biomed Signal Proces, 2014, 9(1):56-69.
[2] JOSHI N, JAIN S. Optimization of nonlocal means filtering technique for denoising magnetic resonance images:A review[C]//Proceedings of fifth international conference on soft computing for problem solving. Singapore:Springer, 2016:1-15.
[3] SAMSONOV A A, JOHNSON C R. Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels[J]. Magn Reson Med, 2004, 52(4):798-806.
[4] LU B B, DENG C, LIU Q, et al. Four order adaptive PDE method for MRI denoising[C]//IEEE ACMT Comput Bi, 2009.
[5] ZHANG F, MA L H. MRI denoising using the anisotropic coupled diffusion equations[C]//20103rd international conference on BMEI. IEEE, 2010, 1:397-401.
[6] MA J, PLONKA G. Combined curvelet shrinkage and nonlinear anisotropic diffusion[J]. IEEE T Image Process, 2007, 16(9):2198-2206.
[7] PARTHIBAN L, SUBRAMANIAN R. Medical image denoising using X-lets[C]//India conference, 2006 Annual IEEE, 2006:1-6.
[8] YANG X, FEI B. A wavelet multiscale denoising algorithm for magnetic resonance (MR) images[J]. Meas Sci Technol, 2011, 22(2):025803.
[9] LUISIER F, BLU T, WOLFE P J. A cure for noisy magnetic resonance images:Chi-square unbiased risk estimation[J]. IEEE T Image Process, 2012, 21(8):3454-3466.
[10] BUADES A, COLL B, MOREL J M. A review of image denoising algorithms, with a new one[J]. Multiscale Model Sim, 2005, 4(2):490-530.
[11] MANJÓN J V, CARBONELL J, LULL J J, et al. MRI denoising using non-local means[J]. Med Image Anal, 2008, 12(4):514-523.
[12] HU J R, PU Y F, WU X, et al. Improved DCT-based nonlocal means filter for MR images denoising[J]. Comput Math Methods Med, 2012:232685.
[13] MANJÓN J V, COUPÉ P, BUADES A, et al. New methods for MRI denoising based on sparseness and self-similarity[J]. Med Image Anal, 2012, 16(1):18-27.
[14] ZHANG X Y, HOU G R, MA J H, et al. Denoising MR images using non-local means filter with combined patch and pixel similarity[J]. PloS one, 2014, 9(6):e100240.
[15] GUO T L, LIU Q G, LUO J H. Filter bank based nonlocal means for denoising magnetic resonance images[J]. Journal of Shanghai Jiaotong University (Science), 2014, 19(1):72-78.
[16] WIEST-DAESSLÉ N, PRIMA S, COUPÉ P, et al. Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI:Applications to DT-MRI[M]. Berlin:Springer Heidelberg, 2008:171-179.
[17] BARNES C, SHECHTMAN E, FINKELSTEIN A, et al. PatchMatch:A randomized correspondence algorithm for structural image editing[J]. ACM Graphic, 2009, 28(3):24.
[18] GIRAUD R, TA V T, PAPADAKIS N, et al. An optimized PatchMatch for multi-scale and multi-feature label fusion[J]. Neuroimage, 2016, 124:770-782.
[19] BARNES C, SHECHTMAN E, GOLDMAN D B, et al. The generalized patchmatch correspondence algorithm[M]. Berlin:Springer Heidelberg, 2010:29-43.
[20] WIEST-DAESSLÉ N, PRIMA S, COUPÉ P, et al. Non-local means variants for denoising of diffusion-weighted and diffusion tensor MRI[M]. Berlin:Springer Heidelberg, 2007:344-351.
[21] BAO L, ROBINI M, LIU W, et al. Structure-adaptive sparse denoising for diffusion-tensor MRI[J]. Med Image Anal, 2013, 17(4):442-457.
[22] MANJÓN J V, COUPÉ P, BUADES A, et al. Non-local MRI upsampling[J]. Med Image Anal, 2010, 14(6):784-792.
[23] http://brainweb.bic.mni.mcgill.ca/brainweb/
[24] BUADES A, COLL B, MOREL J M. A non-local algorithm for image denoising[C]. IEEE T Comput, 2005, 2:60-65.
[25] COUPÉ P, YGER P, PRIMA S, et al. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images[J]. IEEE T Med Imaging, 2008, 27(4):425-441.
[26] AVANAKI A N, DIYANAT A, SODAGARI S. Optimum parameter estimation for non-local means image de-noising using corner information[C]. IEEE T Signal Proce, 2008:861-863.