Overall Pipeline

Intensity normalization

  • Conventional MRI intensites (T1-w, T2-w, PD, FLAIR) are acquired in arbitrary units
  • Images are not comparable across scanners, subjects, and visits, even when the same protocol is used.
    • This affects algorithm performance, prediction, inference.
    • Even simple things like thresholding an image
  • Intensity normalization brings the intensities to a common scale across people.
  • In this tutorial we will normalize intensities within subject using two methods:
    • Whole-brain normalization
    • White Stripe normalization (Shinohara et al. 2014).

Visualizing whole-brain intensities (each line is a person)

  • We will work with the T1-w images from the training data.
  • Full brain densities are mixtures of the three tissue class distributions.

Visualizing the intensities by tissue class

And these are all the same scanner/protocol!

Whole-brain normalization

  • Let’s Z-score each voxel using mean \(\mu_{WB}\) and standard deviation \(\sigma_{WB}\) computed from all voxels in the brain mask.

\[ T1_{WB} = \frac{T1 - \mu_{WB}}{\sigma_{WB}}\]

  • zscore_img is a function in neurobase that does this.
  • It takes an image and a binary mask. The default is to use all voxels in the brain mask.
zscore_img(img = img, mask = mask)

Whole-brain normalized intensities