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Premium Comparison of gradient encoding directions for higher order tensor diffusion data
Mang Sarah C.,
Gembris Daniel,
Grodd Wolfgang,
Klose Uwe
Publication year2009
Publication title
magnetic resonance in medicine
Resource typeJournals
PublisherWiley Subscription Services
Abstract Recently, higher order tensors were proposed for a more advanced representation of non‐Gaussian diffusion. These advanced diffusion models have new requirements for the gradient encoding schemes used in the diffusion weighted image acquisition. The influence of the gradient encoding schemes on the estimated standard second order diffusion tensor was previously investigated. Here, we focus on the suitability of different encoding scheme types for higher order tensor models. Two quality measures for the gradient encoding schemes, the condition number of the estimation matrix and a new measure that evaluates the signal deviation on simulated data, are used to determine which gradient encoding is suited best for higher order tensor estimations. Six different gradient encoding scheme types were investigated. A certain force‐minimizing scheme type gave the best results in the evaluations presented here. Magn Reson Med 61:335–343, 2009. © 2009 Wiley‐Liss, Inc.
Subject(s)algorithm , artificial intelligence , computer science , diffusion , diffusion mri , encoding (memory) , focus (optics) , gaussian , geometry , law , magnetic resonance imaging , mathematics , medicine , optics , physics , political science , politics , quantum mechanics , radiology , representation (politics) , tensor (intrinsic definition) , thermodynamics
SCImago Journal Rank1.696

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