In MRI, images of the same contrast (e.g., T1) from the same subject can show 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, in order 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 of multiple characteristics for deep learning training and may still be unsuccessful when applied to images of an unseen domain. 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 of harmonizing unseen domain images. 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. BlindHarmony was evaluated using simulated and real datasets and compared with conventional methods. BlindHarmony achieved a noticeable performance in both datasets, highlighting its potential for future use in clinical settings.

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的相同对比度（如T1）图像可能呈现显著差异。这些图像差异形成领域鸿沟，需通过称为图像谐波化的步骤加以弥合，以便利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，但其通常需要多特征数据集进行深度学习训练，且应用于未见领域图像时仍可能失败。为解决这一局限，我们提出名为"盲谐波化"的新概念——该方法仅利用目标领域数据进行训练，却能对未见领域图像实现谐波化处理。为实施盲谐波化，我们开发了基于无条件流模型的BlindHarmony，该模型在目标领域数据上训练。经谐波化处理的图像通过优化保持与输入源领域图像的相关性，同时确保流模型的潜向量接近高斯分布中心。使用模拟数据集和真实数据集对BlindHarmony进行评估，并与传统方法进行对比。BlindHarmony在两个数据集中均取得显著性能，凸显其未来应用于临床环境的潜力。