  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.
