title: Experimentally and computationally fast method for estimation of a mean kurtosis.
abstract: OBJECTIVE
  Results from several recent studies suggest the magnetic resonance diffusion-derived metric mean kurtosis (MK) to be a sensitive marker for tissue pathology; however, lengthy acquisition and postprocessing time hamper further exploration. The purpose of this study is to introduce and evaluate a new MK metric and a rapid protocol for its estimation.
  
  
  METHODS
  The protocol requires acquisition of 13 standard diffusion-weighted images, followed by linear combination of log diffusion signals, thus avoiding nonlinear optimization. The method was evaluated on an ex vivo rat brain and an in vivo human brain. Parameter maps were compared with MK estimated from a standard diffusion kurtosis imaging (DKI) data set comprising 160 diffusion-weighted images.
  
  
  RESULTS
  The new MK displays remarkably similar contrast to MK, and the proposed protocol acquires the necessary data in less than 1 min for full human brain coverage, with a postprocessing time of a few seconds. Scan-rescan reproducibility was comparable with MK.
  
  
  CONCLUSIONS
  The framework offers a robust and rapid method for estimating MK, with a protocol easily adapted on commercial scanners, as it requires only minimal modification of standard diffusion-weighting protocols. These properties make the method feasible in practically any clinical setting.
authors.affiliation: Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark.
authors.forename: Brian
authors.initials: B
authors.lastname: Hansen
authors.forename: Torben E
authors.initials: TE
authors.lastname: Lund
authors.forename: Ryan
authors.initials: R
authors.lastname: Sangill
authors.forename: Sune Nørhøj
authors.initials: SN
authors.lastname: Jespersen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity