Effects of signal - to - noise ratio on the accuracy and reproducibility of diffusion tensor imaging - derived fractional anisotropy, mean diffusivity, and
principal eigenvector measurements at 1.5 T.
Not exact matches
To select a small number of parameters conveying the maximal information about the [photon] shower shape, we uncorrelated the above parameters through a
principal component analysis in which these seven parameters are transformed into new seven uncorrelated parameters given by the
eigenvectors of the covariance matrix.
The first
eigenvector of the covariance matrix for this simulation is the red curve in Figure 9 - 2, showing the precise form of the spurious trend that the
principal component would introduce into the fitted model in this case.
FIGURE 9 - 2 Five simulated
principal components and the corresponding population
eigenvector.