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A simple method for rectified noise floor suppression: Phase‐corrected real data reconstruction with application to diffusion‐weighted imaging
Author(s) -
Prah Douglas E.,
Paulson Eric S.,
Nencka Andrew S.,
Schmainda Kathleen M.
Publication year - 2010
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.22407
Subject(s) - noise (video) , diffusion , signal to noise ratio (imaging) , phase (matter) , signal (programming language) , weighting , diffusion mri , communication noise , computer science , nuclear magnetic resonance , mathematics , artificial intelligence , physics , statistics , acoustics , magnetic resonance imaging , image (mathematics) , medicine , linguistics , philosophy , radiology , quantum mechanics , thermodynamics , programming language
Diffusion‐weighted MRI is an intrinsically low signal‐to‐noise ratio application due to the application of diffusion‐weighting gradients and the consequent longer echo times. The signal‐to‐noise ratio worsens with increasing image resolution and diffusion imaging methods that use multiple and higher b‐values. At low signal‐to‐noise ratios, standard magnitude reconstructed diffusion‐weighted images are confounded by the existence of a rectified noise floor, producing poor estimates of diffusion metrics. Herein, we present a simple method of rectified noise floor suppression that involves phase correction of the real data. This approach was evaluated for diffusion‐weighted imaging data, obtained from ethanol and water phantoms and the brain of a healthy volunteer. The parameter fits from monoexponential, biexponential, and stretched‐exponential diffusion models were computed using phase‐corrected real data and magnitude data. The results demonstrate that this newly developed simple approach of using phase‐corrected real images acts to reduce or even suppress the confounding effects of a rectified noise floor, thereby producing more accurate estimates of diffusion parameters. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.