  Accuracy and consistency are two key factors in computer-assisted magnetic
resonance (MR) image analysis. However, contrast variation from site to site
caused by lack of standardization in MR acquisition impedes consistent
measurements. In recent years, image harmonization approaches have been
proposed to compensate for contrast variation in MR images. Current
harmonization approaches either require cross-site traveling subjects for
supervised training or heavily rely on site-specific harmonization models to
encourage harmonization accuracy. These requirements potentially limit the
application of current harmonization methods in large-scale multi-site studies.
In this work, we propose an unsupervised MR harmonization framework, CALAMITI
(Contrast Anatomy Learning and Analysis for MR Intensity Translation and
Integration), based on information bottleneck theory. CALAMITI learns a
disentangled latent space using a unified structure for multi-site
harmonization without the need for traveling subjects. Our model is also able
to adapt itself to harmonize MR images from a new site with fine tuning solely
on images from the new site. Both qualitative and quantitative results show
that the proposed method achieves superior performance compared with other
unsupervised harmonization approaches.
