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Diffusion measurements and diffusion tensor imaging with noisy magnitude data
Author(s) -
Kristoffersen Anders
Publication year - 2009
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21589
Subject(s) - diffusion mri , estimator , magnitude (astronomy) , diffusion , noise (video) , rician fading , monte carlo method , signal to noise ratio (imaging) , contrast (vision) , mathematics , algorithm , computer science , statistics , physics , magnetic resonance imaging , artificial intelligence , image (mathematics) , medicine , decoding methods , astronomy , fading , radiology , thermodynamics
Purpose To compare an unbiased method for estimation of the diffusion coefficient to the quick, but biased, log‐linear (LL) method in the presence of noisy magnitude data. Materials and Methods The magnitude operation changes the signal distribution in magnetic resonance (MR) images from Gaussian to Rician. If not properly taken into account, this will introduce a bias in the estimated diffusion coefficient. We compare two methods by means of Monte Carlo simulations. The first one applies least‐squares fitting of the measured signal to the median (MD) value of the probability density function. The second method is uncorrected LL estimation. We also perform a high‐resolution diffusion tensor experiment. Results The uncorrected LL estimator is heavily biased at low signal‐to‐noise ratios. The bias has a significant effect on image quality. The MD estimator is accurate and produces images with excellent contrast. Conclusion In the presence of noisy magnitude data, unbiased estimation is essential in diffusion measurements and diffusion tensor imaging. J. Magn. Reson. Imaging 2009;29:237–241. © 2008 Wiley‐Liss, Inc.