Abstract
Deep learning holds immense promise for transforming medical image analysis, yet its clinical generalization remains profoundly limited. A major barrier is data heterogeneity. This is particularly true in Magnetic Resonance Imaging, where scanner hardware differences, diverse acquisition protocols, and varying sequence parameters introduce substantial domain shifts that obscure underlying biological signals. Data harmonization methods aim to reduce these instrumental and acquisition variability, but existing approaches remain insufficient. When applied to imaging data, image-based harmonization approaches are often restricted by the need for target images, while existing text-guided methods rely on simplistic labels that fail to capture complex acquisition details or are typically restricted to datasets with limited variability, failing to capture the heterogeneity of real-world clinical environments. To address these limitations, we propose DIST-CLIP (Disentangled Style Transfer with CLIP Guidance), a unified framework for MRI harmonization that flexibly uses either target images or DICOM metadata for guidance. Our framework explicitly disentangles anatomical content from image contrast, with the contrast representations being extracted using pre-trained CLIP encoders. These contrast embeddings are then integrated into the anatomical content via a novel Adaptive Style Transfer module. We trained and evaluated DIST-CLIP on diverse real-world clinical datasets, and showed significant improvements in performance when compared against state-of-the-art methods in both style translation fidelity and anatomical preservation, offering a flexible solution for style transfer and standardizing MRI data. Our code and weights will be made publicly available upon publication.
Abstract (translated)
深度学习在医学图像分析方面展现出巨大的潜力，然而其临床适用性的推广仍然受到严重限制。一个主要障碍是数据异质性问题，在磁共振成像（MRI）中尤其明显。MRI扫描仪硬件差异、多样的采集协议和不同的序列参数引入了显著的领域偏移，从而掩盖了潜在的生物学信号。为了减少这些仪器和采集过程中的变异性，人们开发了一系列数据校准方法，但现有的方法仍然不够完善。在应用于图像数据时，基于图像的方法通常受到需要目标图像限制，而现有文本引导的方法则依赖于简单的标签，无法捕捉复杂的采集细节或仅限于具有有限变异性的数据集，从而不能适应现实世界临床环境中的异质性。 为了解决这些问题，我们提出了一种名为DIST-CLIP（使用CLIP指导的解耦风格迁移）的统一框架，用于MRI校准。该框架灵活地利用目标图像或DICOM元数据进行引导，并明确分离出解剖内容和图像对比度。使用预先训练好的CLIP编码器从输入图像中提取对比度表示，这些对比度嵌入随后通过新颖的自适应风格迁移模块融入到解剖结构当中。 我们在多种真实世界的临床数据集上对DIST-CLIP进行了训练和评估，并显示了其在风格翻译准确性和解剖细节保留方面的性能相比现有最先进的方法有了显著提高。这为MRI图像的数据标准化提供了一个灵活且高效的解决方案。我们的代码和权重将在发表后公开发布。
URL
https://arxiv.org/abs/2512.07674
