In MRI studies, the aggregation of imaging data from multiple acquisition sites enhances sample size but may introduce site-related variabilities that hinder consistency in subsequent analyses. Deep learning methods for image translation have emerged as a solution for harmonizing MR images across sites. In this study, we introduce IGUANe (Image Generation with Unified Adversarial Networks), an original 3D model that leverages the strengths of domain translation and straightforward application of style transfer methods for multicenter brain MR image harmonization. IGUANe extends CycleGAN by integrating an arbitrary number of domains for training through a many-to-one architecture. The framework based on domain pairs enables the implementation of sampling strategies that prevent confusion between site-related and biological variabilities. During inference, the model can be applied to any image, even from an unknown acquisition site, making it a universal generator for harmonization. Trained on a dataset comprising T1-weighted images from 11 different scanners, IGUANe was evaluated on data from unseen sites. The assessments included the transformation of MR images with traveling subjects, the preservation of pairwise distances between MR images within domains, the evolution of volumetric patterns related to age and Alzheimer’s disease (AD), and the performance in age regression and patient classification tasks. Comparisons with other harmonization and normalization methods suggest that IGUANe better preserves individual information in MR images and is more suitable for maintaining and reinforcing variabilities related to age and AD. Future studies may further assess IGUANe in other multicenter contexts, either using the same model or retraining it for applications to different image modalities. Codes and the trained IGUANe model are available at https://github.com/RocaVincent/iguane_harmonization.git.

中文翻译：

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IGUANe：用于大脑 MR 图像多中心协调的 3D 可通用 CycleGAN


在 MRI 研究中，来自多个采集位点的成像数据的聚合增加了样本量，但可能会引入与位点相关的变异性，从而阻碍后续分析的一致性。用于图像翻译的深度学习方法已成为跨站点协调 MR 图像的解决方案。在这项研究中，我们介绍了 IGUANe（使用统一对抗网络生成图像），这是一种原始的 3D 模型，它利用域转换的优势和风格转移方法的直接应用来实现多中心脑 MR 图像协调。IGUANe 通过多对一架构集成任意数量的域进行训练，从而扩展了 CycleGAN。基于域对的框架能够实施采样策略，以防止位点相关变异和生物变异之间的混淆。在推理过程中，该模型可以应用于任何图像，甚至可以应用于来自未知采集点的图像，使其成为用于协调的通用生成器。IGUANe 在包含来自 11 个不同扫描仪的 T1 加权图像的数据集上进行训练，并根据来自未见过站点的数据进行评估。评估包括移动受试者的 MR 图像的转换、域内 MR 图像之间成对距离的保留、与年龄和阿尔茨海默病 （AD） 相关的体积模式的演变，以及年龄回归和患者分类任务的表现。与其他协调和归一化方法的比较表明，IGUANe 更好地保留了 MR 图像中的个人信息，并且更适合维持和加强与年龄和 AD 相关的变异性。 未来的研究可能会在其他多中心环境中进一步评估 IGUANe，要么使用相同的模型，要么重新训练它以应用于不同的图像模式。代码和经过训练的 IGUANe 模型可在 https://github.com/RocaVincent/iguane_harmonization.git 获得。