title: Comparison of gradient encoding directions for higher order tensor diffusion data.
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.
authors.affiliation: Section Experimental MR of CNS, Diagnostic and Interventional Neuroradiology, University Hospital Tuebingen, Tuebingen, Germany. sarah.mang@med.uni-tuebingen.de
authors.forename: Sarah C
authors.initials: SC
authors.lastname: Mang
authors.forename: Daniel
authors.initials: D
authors.lastname: Gembris
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Grodd
authors.forename: Uwe
authors.initials: U
authors.lastname: Klose
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D008962:Models, Theoretical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity