In MRI, images of the same contrast (e.g., T$_1$) from the same subject can exhibit noticeable differences when acquired using different hardware, sequences, or scan parameters. These differences in images create a domain gap that needs to be bridged by a step called image harmonization, to process the images successfully using conventional or deep learning-based image analysis (e.g., segmentation). Several methods, including deep learning-based approaches, have been proposed to achieve image harmonization. However, they often require datasets from multiple domains for deep learning training and may still be unsuccessful when applied to images from unseen domains. To address this limitation, we propose a novel concept called `Blind Harmonization', which utilizes only target domain data for training but still has the capability to harmonize images from unseen domains. For the implementation of blind harmonization, we developed BlindHarmony using an unconditional flow model trained on target domain data. The harmonized image is optimized to have a correlation with the input source domain image while ensuring that the latent vector of the flow model is close to the center of the Gaussian distribution. BlindHarmony was evaluated on both simulated and real datasets and compared to conventional methods. BlindHarmony demonstrated noticeable performance on both datasets, highlighting its potential for future use in clinical settings. The source code is available at: https://github.com/SNU-LIST/BlindHarmony

翻译：在磁共振成像中，同一受试者使用不同硬件、序列或扫描参数获取相同对比度（如T₁）的图像时，可能会表现出显著差异。这些图像差异造成了域差距，需要通过称为图像谐波化的步骤来弥合，以便使用传统或基于深度学习的图像分析（如分割）成功处理图像。目前已提出多种方法（包括基于深度学习的方法）实现图像谐波化。然而，这些方法通常需要来自多个域的数据集进行深度学习训练，并且在应用于未见域图像时仍可能失败。为解决这一局限，我们提出一种名为“盲谐波化”的新概念，该方法仅利用目标域数据进行训练，却仍具备对未见域图像进行谐波化的能力。为实现盲谐波化，我们开发了BlindHarmony，其使用在目标域数据上训练的无条件流模型。谐波化图像通过优化使输入源域图像与其保持相关性，同时确保流模型的潜在向量接近高斯分布的中心。我们在模拟数据集和真实数据集上对BlindHarmony进行了评估，并与传统方法进行了比较。结果表明，BlindHarmony在两个数据集上均展现出显著性能，凸显了其未来在临床环境中应用的潜力。源代码可在以下网址获取：https://github.com/SNU-LIST/BlindHarmony