Abstract
Aggregating multi-site brain MRI data can enhance deep learning model training, but also introduces non-biological heterogeneity caused by site-specific variations (e.g., differences in scanner vendors, acquisition parameters, and imaging protocols) that can undermine generalizability. Recent retrospective MRI harmonization seeks to reduce such site effects by standardizing image style (e.g., intensity, contrast, noise patterns) while preserving anatomical content. However, existing methods often rely on limited paired traveling-subject data or fail to effectively disentangle style from anatomy. Furthermore, most current approaches address only single-sequence harmonization, restricting their use in real-world settings where multi-sequence MRI is routinely acquired. To this end, we introduce MMH, a unified framework for multi-site multi-sequence brain MRI harmonization that leverages biomedical semantic priors for sequence-aware style alignment. MMH operates in two stages: (1) a diffusion-based global harmonizer that maps MR images to a sequence-specific unified domain using style-agnostic gradient conditioning, and (2) a target-specific fine-tuner that adapts globally aligned images to desired target domains. A tri-planar attention BiomedCLIP encoder aggregates multi-view embeddings to characterize volumetric style information, allowing explicit disentanglement of image styles from anatomy without requiring paired data. Evaluations on 4,163 T1- and T2-weighted MRIs demonstrate MMH's superior harmonization over state-of-the-art methods in image feature clustering, voxel-level comparison, tissue segmentation, and downstream age and site classification.
Abstract (translated)
跨多个站点的脑部MRI数据聚合可以增强深度学习模型训练，但也会引入由于特定地点变化（如不同制造商、采集参数和成像协议差异）导致的非生物异质性，从而削弱了模型的泛化能力。最近回顾性的MRI校准试图通过标准化图像风格（例如强度、对比度、噪声模式）来减少此类站点效应，同时保留解剖内容。然而，现有方法往往依赖于有限的一对一旅行者数据或无法有效区分风格与解剖结构。此外，大多数当前的方法仅处理单序列的校准，限制了它们在现实场景中的应用，因为在这些场景中通常会获取多序列MRI图像。 为此，我们引入了一个统一框架MMH，用于跨多个站点和多序列脑部MRI图像的校准，该框架利用生物医学语义先验进行序列感知风格对齐。MMH分为两个阶段操作：（1）一个基于扩散的整体校准器，使用无风格特征导向梯度条件将MR图像映射到特定于序列的统一领域；（2）针对目标领域的特化微调器，它将全局对齐的图像调整为所需的目标领域。三平面注意力BiomedCLIP编码器通过聚合多视图嵌入来表征体积样式信息，从而在无需配对数据的情况下明确区分图像风格与解剖结构。 基于4,163张T1和T2加权MRI图像进行评估表明，MMH在图像特征聚类、体素级比较、组织分割以及下游年龄和站点分类方面，均优于现有最佳方法。
URL
https://arxiv.org/abs/2601.08193
