Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method consists of joint training of an image synthesis network together with separate registration networks and allows adversarial training conditioned on the input even with misaligned data. The work lowers the bar for new clinical applications by allowing effortless training of cross-modality image synthesis networks for more difficult data sets.

翻译：跨模态图像合成是一个活跃的研究领域，具有多个临床相关的医学应用。近年来，允许使用配对但未对齐数据进行训练的方法开始出现。然而，目前尚无能够广泛适用于真实世界数据集的稳健且高性能的方法。在本工作中，我们通过引入新的变形等变损失函数，提出了一种针对配对但非对齐数据的跨模态图像合成问题的通用解决方案。该方法联合训练一个图像合成网络与多个独立的配准网络，即使在未对齐数据条件下，也能实现基于输入的对抗性训练。该工作降低了新临床应用的入门门槛，使得跨模态图像合成网络能够轻松地针对更困难的数据集进行训练。