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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Veröffentlicht in:Magnetic resonance in medicine Jg. 75; H. 1; S. 433 - 440
Hauptverfasser: Lam, Fan, Liu, Ding, Song, Zhuang, Schuff, Norbert, Liang, Zhi-Pei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Blackwell Publishing Ltd 01.01.2016
Wiley Subscription Services, Inc
Schlagworte:
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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Zusammenfassung: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.
Bibliographie: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
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.25643