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Denoising diffusion‐weighted magnitude MR images using rank and edge constraints
Author(s) -
Lam Fan,
Babacan S. Derin,
Haldar Justin P.,
Weiner Michael W.,
Schuff Norbert,
Liang ZhiPei
Publication year - 2014
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.24728
Subject(s) - noise reduction , diffusion mri , noise (video) , diffusion , maximum a posteriori estimation , mathematics , signal to noise ratio (imaging) , algorithm , computer science , artificial intelligence , pattern recognition (psychology) , mathematical optimization , image (mathematics) , statistics , magnetic resonance imaging , physics , medicine , maximum likelihood , radiology , thermodynamics
Purpose To improve signal‐to‐noise ratio for diffusion‐weighted magnetic resonance images. Methods A new method is proposed for denoising diffusion‐weighted magnitude images. The proposed method formulates the denoising problem as an maximum a posteriori} estimation problem based on Rician/noncentral χ likelihood models, incorporating an edge prior and a low‐rank model. The resulting optimization problem is solved efficiently using a half‐quadratic method with an alternating minimization scheme. Results The performance of the proposed method has been validated using simulated and experimental data. Diffusion‐weighted images and noisy data were simulated based on the diffusion tensor imaging model and Rician/noncentral χ distributions. The simulation study (with known gold standard) shows substantial improvements in single‐to‐noise ratio and diffusion tensor estimation after denoising. In vivo diffusion imaging data at different b ‐values were acquired. Based on the experimental data, qualitative improvement in image quality and quantitative improvement in diffusion tensor estimation were demonstrated. Additionally, the proposed method is shown to outperform one of the state‐of‐the‐art nonlocal means‐based denoising algorithms, both qualitatively and quantitatively. Conclusion The single‐to‐noise ratio of diffusion‐weighted images can be effectively improved with rank and edge constraints, resulting in an improvement in diffusion parameter estimation accuracy. Magn Reson Med 71:1272–1284, 2014. © 2013 Wiley Periodicals, Inc.