PURPOSE: 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. CONCLUSION: 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.
PURPOSE: 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. CONCLUSION: 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: Peter Kochunov; Erin W Dickie; Joseph D Viviano; Jessica Turner; Peter B Kingsley; Neda Jahanshad; Paul M Thompson; Meghann C Ryan; Els Fieremans; Dmitry Novikov; Jelle Veraart; Elliot L Hong; Anil K Malhotra; Robert W Buchanan; Sofia Chavez; Aristotle N Voineskos Journal: Hum Brain Mapp Date: 2017-11-27 Impact factor: 5.038
Authors: B F Kjølby; A R Khan; A Chuhutin; L Pedersen; J B Jensen; S Jakobsen; D Zeidler; R Sangill; J R Nyengaard; S N Jespersen; B Hansen Journal: NMR Biomed Date: 2016-10-12 Impact factor: 4.044
Authors: A Tietze; M B Hansen; L Østergaard; S N Jespersen; R Sangill; T E Lund; M Geneser; M Hjelm; B Hansen Journal: AJNR Am J Neuroradiol Date: 2015-05-14 Impact factor: 3.825