• 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)

• Gray matter distributions are more comparable.

• White matter distributions are more comparable.

• Whole-brain normalization may be sensitive to outliers.
• Lesions in MS can have very high intensities, which lead to bad estimates of mean/variance
  • Other more robust transformations may be used, such as using the median to center, IQR to scale, etc.
• White Stripe (Shinohara et al. 2014) is based on parameters obtained from a sample of normal appearing white matter (NAWM), which is robust to outliers.
  • The idea is to make normal appearing white matter comparable across subjects and visits.

Procedure:

1. Find white matter area on histogram

Procedure:

1. Find white matter area on histogram

2. Estimate mean \(\mu_{WS}\) and variance \(\sigma_{WS}\) of voxel intensities in that area

3. Normalize with those means/variances: \[ T1_{WS} = \frac{T1 - \mu_{WS}}{\sigma_{WS}}\]

• After normalization, NAWM will have a standard normal distribution and units will be in standard deviations of NAWM.
• Gray matter and CSF distributions may not be comparable after White Stripe.

Procedure:

1. Find white matter area on histogram

2. Estimate mean \(\mu_{WS}\) and variance \(\sigma_{WS}\) of voxel intensities in that area

3. Normalize with those means/variances: \[ T1_{WS} = \frac{T1 - \mu_{WS}}{\sigma_{WS}}\]

• After normalization, NAWM will have a standard normal distribution and units will be in standard deviations of NAWM.
• Gray matter and CSF distributions may not be comparable after White Stripe.

library(WhiteStripe)
ind = whitestripe(img = t1, type = "T1", stripped = TRUE)$whitestripe.ind
ws_t1 = whitestripe_norm(t1, indices = ind)

• The whitestripe function takes an image, image type (in our case T1), and a logical indicating whether the image has been skull stripped.
• The indicies of voxels in the NAWM used for estimating the normalization parameters are located in the list element $whitestripe.ind.
• The function whitestripe_norm takes an image and the indicies from a call to whitestripe and returns the White Stripe normalized image as a nifti.

• Intensity normalization is an important step in any image analysis with more than one subject or time point to ensure comparability across images.
• White Stripe normalization may work better and have better interpretation than whole-brain normalization for subsequent lesion segmentation algorithms and analysis.
• Other intensity normalization methods that make intensites comparable across subjects for all tissues exist.
  • RAVEL, which is an extension of WhiteStripe is one example (Fortin et al. 2016).
  • Located at https://github.com/Jfortin1/RAVEL
    • This was shown to have better comparability than histogram matching

http://johnmuschelli.com/imaging_in_r