Maryam Afzali1,2
1Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom, 2Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
Synopsis
Diffusion MRI is a non-invasive technique to study tissue microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using biophysical modeling. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements.
The sensitivity of diffusion MRI to microstructural properties
Diffusion MRI provides a tool to study tissue microstructure based on the Brownian motion of water molecules (Tanner, 1979; Le Bihan et al., 1988) and it is therefore sensitive to differences in the microstructure of the tissue (Callaghan et al., 1988, Basser et al., 1994b, Jones, 2010). The images acquired using diffusion MRI are at the scale of mm while the features that we are interested in such as anisotropy and cell size are at the scale of the micrometer. Diffusion MRI sensitizes the signal to the random motion of the water molecules in a diffusion time from milliseconds up to one second. At room or body temperature, the mean displacement due to motion over this time scale is at the scale of the micrometer, which is the cellular scale. Therefore the cellular structure of the tissue directly affects the motion of the water molecules, so diffusion MRI is a useful tool to study the tissue microstructure.
In this technique, the images are acquired with a different number of directions, b-values, b-tensor encoding schemes (Callaghan et al., 1988, Basser et al., 1994b, Jones, 2010, Westin et al., 2016). Then a model is fitted to the signal and a set of parameters can be obtained for each voxel in the image—either for signal representations (e.g. DT-MRI) (Basser et al., 1994b) or modeling (Stanisz et al., 1997). These parameters are related to the microstructural properties of the tissue. Signal representations versus Multi-compartment models
Most diffusion MRI based analysis of microstructure falls into two categories: a model of the signal to compute quantitative physical properties of the diffusion and representations of the tissue to acquire tissue-specific metrics.
Techniques based on the representation of the signal focus on delineating the diffusion signal attenuation without explicitly considering the underlying tissues that create this attenuation. The most widely used, DT-MRI, employs a tensor to characterize the Gaussian distribution of displacements and MRI signal decay (Basser et al., 1994b, Basser et al., 1994a). Diffusion Kurtosis Imaging (DKI) is obtained when the kurtosis term in addition to the covariance term is preserved in the expansion (Jensen et al., 2005). A recently proposed signal-based framework called Mean Apparent Propagator (MAP)-MRI uses a series of basis functions to fit the three-dimensional q-space signal and transform it into diffusion propagators (Özarslan et al., 2013).
Many microstructure models developed over the years for interpreting the diffusion MR data employ a multi-compartmental approach wherein the signal is written as the sum of contributions from different structures making up the tissue (Stanisz et al., 1997; Alexander, 2008; Alexander et al., 2010; Kaden et al., 2016b; Panagiotaki et al., 2012; Scherrer et al., 2016).
Models usually consider biophysical influences on the signal. However, using the available practical acquisitions, a small set of parameters can be estimated. Some constraints such as fixing parameters, ignoring some effects (Jelescu et al., 2016), enforcing the relationship between the model parameters, or imposing prior distribution may bias the estimation of the remaining parameters.Acknowledgements
This work was funded by Wellcome Trust Investigator Award (219536/Z/19/Z).References
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