Information-based Disentangled Representation Learning for Unsupervised MR Harmonization
This work addresses the challenge of multi-site MR image analysis by enabling more consistent measurements, though it appears incremental as it builds on existing unsupervised harmonization approaches.
The authors tackled the problem of inconsistent MR image contrast across different sites by proposing an unsupervised harmonization framework called CALAMITI, which achieved superior performance compared to other unsupervised methods without requiring traveling subjects.
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.