## 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)