Literature DB >> 31425799

Reproducibility of axonal water fraction derived from the spherical mean diffusion weighted signal.

Jucai Song1, Ho Ming Chow2, Rahul Nikam3, Vinay Kandula3, Arabinda K Choudhary3, Hua Li4.   

Abstract

Recent years have seen growing interest in measuring axonal water fraction (AWF) using the spherical mean diffusion weighted signal, but information about the reproducibility of this method is needed before applying it in large-scale studies. The current study aims to evaluate the reproducibility of AWF derived from the spherical mean signal method. This retrospective study analyzed the Human Connectome Project (HCP) test-retest diffusion data of ten healthy adults. The diffusion scan was performed two times for each subject. Diffusion tensor imaging-based fractional anisotropy (FA) was calculated with b = 1000 s/mm2. AWF was calculated with b = 3000 s/mm2 using the spherical mean signal method. Gradient nonlinearities were corrected in both methods. Reproducibility was assessed using the reproducibility error, which is the percent absolute change relative to the mean. The mean reproducibility error of fractional anisotropy (FA) is 9.7 ± 1.0% in white matter and 18.0 ± 2.0% in gray matter. The mean reproducibility error of AWF is 4.6 ± 0.6% in white matter and 7.0 ± 1.5% in gray matter. Spherical mean signal-based AWF is more reproducible than FA for the HCP high resolution, low signal-to-noise ratio diffusion data.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Axonal water fraction; Diffusion tensor imaging; Reproducibility; Spherical mean signal

Mesh:

Substances:

Year:  2019        PMID: 31425799      PMCID: PMC8781288          DOI: 10.1016/j.mri.2019.08.024

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  39 in total

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Review 3.  The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development.

Authors:  Brittany R Howell; Martin A Styner; Wei Gao; Pew-Thian Yap; Li Wang; Kristine Baluyot; Essa Yacoub; Geng Chen; Taylor Potts; Andrew Salzwedel; Gang Li; John H Gilmore; Joseph Piven; J Keith Smith; Dinggang Shen; Kamil Ugurbil; Hongtu Zhu; Weili Lin; Jed T Elison
Journal:  Neuroimage       Date:  2018-03-22       Impact factor: 6.556

4.  Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging.

Authors:  Jens H Jensen; Joseph A Helpern; Anita Ramani; Hanzhang Lu; Kyle Kaczynski
Journal:  Magn Reson Med       Date:  2005-06       Impact factor: 4.668

5.  TE dependent Diffusion Imaging (TEdDI) distinguishes between compartmental T2 relaxation times.

Authors:  Jelle Veraart; Dmitry S Novikov; Els Fieremans
Journal:  Neuroimage       Date:  2017-09-19       Impact factor: 6.556

6.  Minimal number of gradient directions for robust measurement of spherical mean diffusion weighted signal.

Authors:  Hua Li; Ho Ming Chow; Diane C Chugani; Harry T Chugani
Journal:  Magn Reson Imaging       Date:  2018-08-30       Impact factor: 2.546

7.  Stability of MRI metrics in the advanced research core of the NCAA-DoD concussion assessment, research and education (CARE) consortium.

Authors:  Andrew S Nencka; Timothy B Meier; Yang Wang; L Tugan Muftuler; Yu-Chien Wu; Andrew J Saykin; Jaroslaw Harezlak; M Alison Brooks; Christopher C Giza; John Difiori; Kevin M Guskiewicz; Jason P Mihalik; Stephen M LaConte; Stefan M Duma; Steven Broglio; Thomas McAllister; Michael A McCrea; Kevin M Koch
Journal:  Brain Imaging Behav       Date:  2018-08       Impact factor: 3.978

8.  Free water elimination improves test-retest reproducibility of diffusion tensor imaging indices in the brain: A longitudinal multisite study of healthy elderly subjects.

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Journal:  Hum Brain Mapp       Date:  2016-08-13       Impact factor: 5.038

Review 9.  The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites.

Authors:  B J Casey; Tariq Cannonier; May I Conley; Alexandra O Cohen; Deanna M Barch; Mary M Heitzeg; Mary E Soules; Theresa Teslovich; Danielle V Dellarco; Hugh Garavan; Catherine A Orr; Tor D Wager; Marie T Banich; Nicole K Speer; Matthew T Sutherland; Michael C Riedel; Anthony S Dick; James M Bjork; Kathleen M Thomas; Bader Chaarani; Margie H Mejia; Donald J Hagler; M Daniela Cornejo; Chelsea S Sicat; Michael P Harms; Nico U F Dosenbach; Monica Rosenberg; Eric Earl; Hauke Bartsch; Richard Watts; Jonathan R Polimeni; Joshua M Kuperman; Damien A Fair; Anders M Dale
Journal:  Dev Cogn Neurosci       Date:  2018-03-14       Impact factor: 6.464

10.  Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence.

Authors:  Erhan Genç; Christoph Fraenz; Caroline Schlüter; Patrick Friedrich; Rüdiger Hossiep; Manuel C Voelkle; Josef M Ling; Onur Güntürkün; Rex E Jung
Journal:  Nat Commun       Date:  2018-05-15       Impact factor: 14.919

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