变形图像配准是分析医学图像的基本过程之一。特别是，在诊断肝癌和淋巴瘤等腹部疾病时，经常使用从不同模式或不同成像协议扫描的多域图像。然而，由于扫描时间、患者呼吸、运动等原因，它们并没有对齐。尽管最近基于学习的方法可以高性能地实时提供变形，但使用深度学习的多域腹部图像配准仍然具有挑战性，因为图像不同领域具有不同的特征，例如图像对比度和强度范围。为了解决这个问题，本文提出了一种使用神经最优传输的新型无监督多域图像配准框架，称为 OTMorph。当移动和固定体积作为输入时，我们提出的模型的传输模块学习最佳传输计划以将数据分布从移动体积映射到固定体积并估计域传输体积。随后，采用传输体积的配准模块可以有效地估计变形场，从而提高变形性能。使用多模态和多参数腹部医学图像进行多域图像配准的实验结果表明，该方法通过域传输图像提供了优异的变形配准，从而减轻了输入图像之间的域间隙。此外，我们甚至在分布外数据上也取得了改进，这表明我们的模型对于各种医学图像的配准具有卓越的通用性。我们的源代码可在 https://github.com/boahK/OTMorph 获取。

Deformable image registration is one of the essential processes in analyzing medical images. In particular, when diagnosing abdominal diseases such as hepatic cancer and lymphoma, multi-domain images scanned from different modalities or different imaging protocols are often used. However, they are not aligned due to scanning times, patient breathing, movement, etc. Although recent learning-based approaches can provide deformations in real-time with high performance, multi-domain abdominal image registration using deep learning is still challenging since the images in different domains have different characteristics such as image contrast and intensity ranges. To address this, this paper proposes a novel unsupervised multi-domain image registration framework using neural optimal transport, dubbed OTMorph. When moving and fixed volumes are given as input, a transport module of our proposed model learns the optimal transport plan to map data distributions from the moving to the fixed volumes and estimates a domain-transported volume. Subsequently, a registration module taking the transported volume can effectively estimate the deformation field, leading to deformation performance improvement. Experimental results on multi-domain image registration using multi-modality and multi-parametric abdominal medical images demonstrate that the proposed method provides superior deformable registration via the domain-transported image that alleviates the domain gap between the input images. Also, we attain the improvement even on out-of-distribution data, which indicates the superior generalizability of our model for the registration of various medical images. Our source code is available at https://github.com/boahK/OTMorph.