A fast algorithm for denoising magnitude diffusion-weighted images with rank and edge constraints

Purpose To accelerate denoising of magnitude diffusion‐weighted images subject to joint rank and edge constraints. Methods We extend a previously proposed majorize‐minimize method for statistical estimation that involves noncentral χ distributions to incorporate joint rank and edge constraints. A ne...

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Vydáno v:	Magnetic resonance in medicine Ročník 75; číslo 1; s. 433 - 440
Hlavní autoři:	Lam, Fan, Liu, Ding, Song, Zhuang, Schuff, Norbert, Liang, Zhi-Pei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Blackwell Publishing Ltd 01.01.2016 Wiley Subscription Services, Inc
Témata:	Algorithms Animals Brain - anatomy & histology Coding Computer applications Computer Simulation Constraints Datasets Decomposition Diffusion diffusion imaging Diffusion Magnetic Resonance Imaging - methods Diffusion rate edge constraint Efficiency High resolution Hippocampus Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods In Vitro Techniques magnitude image denoising majorize-minimize algorithm Models, Statistical Noise reduction noncentral χ distribution Optimization rank constraint Reproducibility of Results Sensitivity and Specificity Signal-To-Noise Ratio Statistics Swine Tracking rank constraint majorize-minimize algorithm noncentral χ distribution magnitude image denoising diffusion imaging edge constraint
ISSN:	0740-3194, 1522-2594, 1522-2594
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Shrnutí:	Purpose To accelerate denoising of magnitude diffusion‐weighted images subject to joint rank and edge constraints. Methods We extend a previously proposed majorize‐minimize method for statistical estimation that involves noncentral χ distributions to incorporate joint rank and edge constraints. A new algorithm is derived which decomposes the constrained noncentral χ denoising problem into a series of constrained Gaussian denoising problems each of which is then solved using an efficient alternating minimization scheme. Results The performance of the proposed algorithm has been evaluated using both simulated and experimental data. Results from simulations based on ex vivo data show that the new algorithm achieves about a factor of 10 speed up over the original Quasi‐Newton‐based algorithm. This improvement in computational efficiency enabled denoising of large datasets containing many diffusion‐encoding directions. The denoising performance of the new efficient algorithm is found to be comparable to or even better than that of the original slow algorithm. For an in vivo high‐resolution Q‐ball acquisition, comparison of fiber tracking results around hippocampus region before and after denoising will also be shown to demonstrate the denoising effects of the new algorithm. Conclusion The optimization problem associated with denoising noncentral χ distributed diffusion‐weighted images subject to joint rank and edge constraints can be solved efficiently using a majorize‐minimize‐based algorithm. Magn Reson Med 75:433–440, 2016. © 2015 Wiley Periodicals, Inc.
Bibliografie:	ArticleID:MRM25643 ark:/67375/WNG-Z5ZJKP4K-5 istex:4CB760817594FA620F85166ED1510E1F0D1D3876 National Institutes of Health - No. NIH-P41-EB015904, NIH-P41-EB001977, and NIH-1RO1-EB013695 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0740-3194 1522-2594 1522-2594
DOI:	10.1002/mrm.25643