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New Insights about Time-varying Diffusivity and its Estimation from Diffusion MRI

Institution:
1Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. lning@bwh.harvard.edu.
2Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
3Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Wiley
Publication Date:
Aug-2017
Journal:
Magn Reson Med
Volume Number:
78
Issue Number:
2
Pages:
763-74
Citation:
Magn Reson Med. 2017 Aug;78(2):763-74.
PubMed ID:
27611013
PMCID:
PMC5344793
Keywords:
autocorrelation function, diffusion MRI, mean-squared displacement, oscillating gradient spin-echo, single-diffusion encoding, time-varying diffusivity
Appears in Collections:
NAC, LMI, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
Generated Citation:
Ning L., Setsompop K., Westin C-F., Rathi Y. New Insights about Time-varying Diffusivity and its Estimation from Diffusion MRI. Magn Reson Med. 2017 Aug;78(2):763-74. PMID: 27611013. PMCID: PMC5344793.
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PURPOSE: Characterizing the relation between the applied gradient sequences and the measured diffusion MRI signal is important for estimating the time-dependent diffusivity, which provides important information about the microscopic tissue structure. THEORY AND METHODS: In this article, we extend the classical theory of Stepišnik for measuring time-dependent diffusivity under the Gaussian phase approximation. In particular, we derive three novel expressions which represent the diffusion MRI signal in terms of the mean-squared displacement, the instantaneous diffusivity, and the velocity autocorrelation function. We present the explicit signal expressions for the case of single diffusion encoding and oscillating gradient spin-echo sequences. Additionally, we also propose three different models to represent time-varying diffusivity and test them using Monte-Carlo simulations and in vivo human brain data. RESULTS: The time-varying diffusivities are able to distinguish the synthetic structures in the Monte-Carlo simulations. There is also strong statistical evidence about time-varying diffusivity from the in vivo human data set. CONCLUSION: The proposed theory provides new insights into our understanding of the time-varying diffusivity using different gradient sequences. The proposed models for representing time-varying diffusivity can be utilized to study time-varying diffusivity using in vivo human brain diffusion MRI data.

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