Abstract
As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals.
Highlights
Diffusion magnetic resonance imaging (MRI) is an important technique for measuring white matter (WM) microstructure in vivo
fractional anisotropy (FA) Values Calculated from Diffusion tensor imaging (DTI), diffusion basis spectrum imaging (DBSI), and Diffusion kurtosis imaging (DKI) Models
We identified systematic relationships between parameters generated by two different models: DKI and DBSI
Summary
Diffusion magnetic resonance imaging (MRI) is an important technique for measuring white matter (WM) microstructure in vivo. There are a variety of techniques to model diffusion MRI data, with the goal of non-invasively deriving quantities that reflect the normal or pathological state of the tissue. Diffusion tensor imaging (DTI) is a classic diffusion MRI modeling technique that models the dispersion of water molecules assuming a Gaussian distribution, which can be visualized in three dimensions as an elliptical isosurface. Extensive studies have shown that DTI is sensitive to microstructural changes, but the measures lack specificity to reflect anatomical complexity and heterogeneous pathology. Different kinds of microstructural disruption result in similar changes to DTI parameters. Either the accumulation of extracellular fluid (edema) or degradation of the myelin sheath (demyelination) can lead to increased MD and RD, as well as decreased FA
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