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
Online-Zugang:	Volltext
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