标题： 磁共振成像中的标准化：采集、图像级和特征级方法综述 显示英文标题

标题： Harmonization in Magnetic Resonance Imaging: A Survey of Acquisition, Image-level, and Feature-level Methods

摘要： 现代医学影像技术极大地推动了神经科学的研究和临床诊断。 然而，跨不同扫描仪、采集协议或影像站点收集的影像数据通常表现出显著的异质性，这被称为“批次效应”或“站点效应”。 这些非生物来源的变异可能会掩盖真实的生物信号，降低可重复性和统计功效，并严重损害基于学习的模型在不同数据集之间的泛化能力。 图像调和旨在消除或减轻此类与站点相关的偏差，同时保留有意义的生物学信息，从而提高数据的可比性和一致性。 本综述全面概述了医学图像调和领域中的关键概念、方法进展、公开可用的数据集、当前挑战和未来方向，重点聚焦于磁共振成像（MRI）。 我们系统地涵盖了整个成像流程，并将调和方法分为前瞻性采集和重建策略、回顾性图像级和特征级方法，以及基于移动受试者的技术。 我们并非提供详尽的综述，而是关注代表性方法，特别强调基于深度学习的方法。 最后，我们总结了仍存在的主要挑战，并提出了未来研究的有前景的方向。 显示英文摘要

摘要： Modern medical imaging technologies have greatly advanced neuroscience research and clinical diagnostics. However, imaging data collected across different scanners, acquisition protocols, or imaging sites often exhibit substantial heterogeneity, known as "batch effects" or "site effects". These non-biological sources of variability can obscure true biological signals, reduce reproducibility and statistical power, and severely impair the generalizability of learning-based models across datasets. Image harmonization aims to eliminate or mitigate such site-related biases while preserving meaningful biological information, thereby improving data comparability and consistency. This review provides a comprehensive overview of key concepts, methodological advances, publicly available datasets, current challenges, and future directions in the field of medical image harmonization, with a focus on magnetic resonance imaging (MRI). We systematically cover the full imaging pipeline, and categorize harmonization approaches into prospective acquisition and reconstruction strategies, retrospective image-level and feature-level methods, and traveling-subject-based techniques. Rather than providing an exhaustive survey, we focus on representative methods, with particular emphasis on deep learning-based approaches. Finally, we summarize the major challenges that remain and outline promising avenues for future research.