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Efficient experimental designs for isotropic generalized diffusion tensor MRI (IGDTI)
Author(s) -
Avram Alexandru V.,
Sarlls Joelle E.,
Hutchinson Elizabeth,
Basser Peter J.
Publication year - 2018
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26656
Subject(s) - diffusion mri , isotropy , nuclear magnetic resonance , tensor (intrinsic definition) , physics , magnetic resonance imaging , mathematics , medicine , geometry , radiology , optics
Purpose We propose a new generalized diffusion tensor imaging (GDTI) experimental design and analysis framework for efficiently measuring orientationally averaged diffusion‐weighted images (DWIs), which remove bulk signal modulations attributed to diffusion anisotropy and quantify isotropic higher‐order diffusion tensors (HOT). We illustrate how this framework accelerates the clinical measurement of rotation‐invariant tissue microstructural parameters derived from HOT, such as the HOT‐Trace and the mean t‐kurtosis. Theory and Methods For a large range of b‐values, we compare orientationally averaged DWIs measured with high angular resolution diffusion imaging to those obtained with the proposed isotropic GDTI (IGDTI) experimental design. We compare rotation‐invariant microstructural parameters measured with IGDTI to those derived from HOTs measured explicitly with GDTI. Results In both fixed‐brain microimaging and in vivo clinical experiments, IGDTI accurately quantifies mean apparent diffusion coefficient (mADC)‐weighted DWIs over a wide range of b‐values and allows efficient computation of HOT‐derived scalar tissue parameters from a small number of DWIs. Conclusions IGDTI provides direct and accurate estimates of orientationally averaged tissue water mobilities over a wide range of b‐values. This efficient method may enable new, sensitive, and quantitative assessments for clinical applications in which changes in mADC can be observe,d such as detecting and characterizing stroke, cancers, and neurodegenerative diseases. Magn Reson Med 79:180–194, 2018. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.