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.
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)