In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches.

在磁共振 (MR) 成像中，采集过程中缺乏标准化通常会导致不同部位的 MR 图像中基于脉冲序列的对比度变化，从而妨碍自动分析中测量的一致性。在本文中，我们提出了一种无监督的 MR 图像协调方法，CALAMITI（MR 强度转换和集成的对比解剖学学习和分析），旨在减轻多站点 MR 成像中的对比度变化。CALAMITI 采用信息瓶颈理论设计，可学习一个全局解缠的潜在空间，其中包含解剖信息和对比信息，从而实现协调。与监督协调方法相比，我们的方法不需要跨站点对样本群体进行成像。与经常遭受几何变化的传统无监督协调方法不同，CALAMITI 通过设计更好地保留了解剖结构。所提出的方法还能够通过简单的微调过程来适应新的测试站点。对从十个站点获取的 MR 图像进行的实验表明，与其他协调方法相比，CALAMITI 具有卓越的性能。