DWI LMMSE Denoising Using Multiple Magnitude Directions_Journal of Biomedical Engineering

Authors：

WUXi , HEJin , ZHUMing

Department of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China;

Corresponding author：

Keywords：

diffusion weighted magnetic resonance imaging; denoising; linear minimum mean square error; multiple magnitude directions; rician noise

DOI：

10.7507/1001-5515.20140002

Video：

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Because of the long acquisition time and spin-echo planar imaging sequence, diffusion weight magnetic resonance image (DWI) should be denoised effectively to ensure the follow-up applications. The commonly used denoising methods which induced from gray level image lack the use of the specific information from multiple magnitude directions. This paper, therefore, proposes a modified linear minimum mean square error (LMMSE) denosing method used for DWI. The proposed method uses the local information to estimate the parameter of the Rician noise and modifies the LMMSE using the information of multiple magnitude directions synthetically. The simulation and experiment of the synthetic DWI and real human brain DWI dataset demonstrate that the proposed method can more effectively remove the Rician noise compared to the commonly used denoising method and improve the robustness and validity of the diffusion tensor magnetic resonance image (DTI).

Citation： WUXi, HEJin, ZHUMing. DWI LMMSE Denoising Using Multiple Magnitude Directions. Journal of Biomedical Engineering, 2014, 31(1): 7-12. doi: 10.7507/1001-5515.20140002 Copy

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12.	KAY S M. Fundamentals of statistical signal processing:Estimation theory[M]. Upper Saddle River, NJ:Prentice-Hall, 1993.
13.	WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4):600-612.

1. LE BIHAN D, JOHANSEN-BERG H. Diffusion MRI at 25:exploring brain tissue structure and function. Neuroimage. 2012 Jun; 61(2):324-341.
2. BASSER P J, MATTIELLO J, LEBIHAN D. MR diffusion tensor spectroscopy and imaging[J]. Biophys J, 1994, 66(1):259-267.
3. BASSER P J, PAJEVIC S. Statistical artifacts in diffusion tensor MRI (DT-MRI) caused by background noise[J]. Magn Reson Med, 2000, 44(1):41-50.
4. TOURNIER J D, MORI S, LEEMANS A. Diffusion tensor imaging and beyond[J]. Magn Reson Med, 2011, 65(6):1532-1556.
5. ANDERSON A W. Theoretical analysis of the effects of noise on diffusion tensor imaging[J]. Magn Reson Med, 2001, 46(6):1174-1188.
6. DING Z, GORE J C, ANDERSON A W. Reduction of noise in diffusion tensor images using anisotropic smoothing[J]. Magn Reson Med, 2005, 53(2):485-490.
7. CHEN B, HSU E W. Noise removal in magnetic resonance diffusion tensor imaging[J]. Magn Reson Med, 2005, 54(2):393-401.
8. SIJBERS J, DEN DEKKER A J. Maximum likelihood estimation of signal amplitude and noise variance from MR data[J]. Magn Reson Imaging, 2004, 51(3):586-594.
9. WIEST-DAESSLÉN, PRIMA S, COUPÉP, et al. Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI:applications to DT-MRI[J]. Med Image Comput Comput Assist Interv. 2008, 11(Pt 2):171-179.
10. MARTIN-FERNANDEZ M, MUÑOZ-MORENO E, CAMMOUN L, et al. Sequential anisotropic multichannel Wiener filtering with Rician bias correction applied to 3D regularization of DWI data[J]. Med Image Anal, 2009, 13(1):19-35.
11. AJA-FERNANDEZ S, NIETHAMMER M, KUBICKI M, et al. Restoration of DWI data using a Rician LMMSE estimator[J]. IEEE Trans Med Imaging, 2008, 27(10):1389-1403.
12. KAY S M. Fundamentals of statistical signal processing:Estimation theory[M]. Upper Saddle River, NJ:Prentice-Hall, 1993.
13. WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4):600-612.

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