• 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).

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

And these are all the same scanner/protocol!

• 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)