  Scanner drift is a well-known magnetic resonance imaging (MRI) artifact
characterized by gradual signal degradation and scan intensity changes over
time. In addition, hardware and software updates may imply abrupt changes in
signal. The combined effects are particularly challenging for automatic image
analysis methods used in longitudinal studies. The implication is increased
measurement variation and a risk of bias in the estimations (e.g. in the volume
change for a structure). We proposed two quite different approaches for scanner
drift normalization and demonstrated the performance for segmentation of knee
MRI using the fully automatic KneeIQ framework. The validation included a total
of 1975 scans from both high-field and low-field MRI. The results demonstrated
that the pre-processing method denoted Atlas Affine Normalization significantly
removed scanner drift effects and ensured that the cartilage volume change
quantifications became consistent with manual expert scores.
