Data Analysis

dVIEWR currently offers two multidimensional diffusion (MDD) analysis methods:

Covariance method

Just like diffusion tensor imaging (DTI), the Covariance method also provides mean diffusivity (MD), fractional anisotropy (FA) and average local orientation maps. In addition, it captures unique measures of microstructural heterogeneity such as microscopic anisotropy and diffusional variance (see available contrasts here). 

In brief, the Covariance method consists of a generalized second-order cumulant expansion of the MDD signal1; this fit can be performed in under a few seconds.

DTD method

The Diffusion tensor distribution (DTD) analysis identifies the nature of the voxel content using a minimal set of assumptions. Its reduced analysis speed, compared to the Covariance method, is counter-balanced by a wealth of additional contrasts.

This method consists of a Monte-Carlo algorithm accounting for multiple solutions that fit the acquired MDD signal2.

1Westin et al., Neuroimage 135, 345-62 (2016)
2Topgaard, NMR Biomed. 32(5):e4066 (2019)