Medical image synthesis remains challenging due to misalignment noise during training. Existing methods have attempted to address this challenge by incorporating a registration-guided module. However, these methods tend to overlook the task-specific constraints on the synthetic and registration modules, which may cause the synthetic module to still generate spatially aligned images with misaligned target images during training, regardless of the registration module's function. Therefore, this paper proposes registration-guided consistency and incorporates disentanglement learning for medical image synthesis. The proposed registration-guided consistency architecture fosters task-specificity within the synthetic and registration modules by applying identical deformation fields before and after synthesis, while enforcing output consistency through an alignment loss. Moreover, the synthetic module is designed to possess the capability of disentangling anatomical structures and specific styles across various modalities. An anatomy consistency loss is introduced to further compel the synthetic module to preserve geometrical integrity within latent spaces. Experiments conducted on both an in-house abdominal CECT-CT dataset and a publicly available pelvic MR-CT dataset have demonstrated the superiority of the proposed method.

本文提出了一种基于注册导向一致性和解缠学习的方法用于医学图像合成，并通过相同的变形场在合成前后应用，并通过对齐损失强制保持输出一致性。此外，合成模块还具备解缠解剖结构和特定风格的能力，引入了解剖一致性损失来进一步保持潜在空间的几何完整性。通过对内部腹部CECT-CT数据集和公开的盆腔MR-CT数据集进行的实验证明了所提方法的优越性。

通过引导一致性与解耦学习提升医学图像合成