Information-based Disentangled Representation Learning for Unsupervised MR Harmonization

TL;DR

This paper introduces CALAMITI, an unsupervised method for MR image harmonization that learns disentangled representations, enabling effective multi-site harmonization without requiring traveling subjects and adaptable to new sites.

Contribution

The paper presents a novel unsupervised framework based on information bottleneck theory that learns disentangled latent spaces for multi-site MR harmonization without supervision.

Findings

Outperforms existing unsupervised harmonization methods.

Learns a unified representation for multi-site MR images.

Adapts to new sites with minimal fine-tuning.

Abstract

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.