Rethinking CT Synthesis through Semantics-Structure Alignment

Mask-guided CT synthesis bridges structural annotations with imaging appearance, facilitating data augmentation and clinical tasks such as radiotherapy planning. Despite plausible visual realism, current methods seek optimal solutions by treating all tissues uniformly, failing to capture intrinsic CT properties such as tissue-specific distributions and strict anatomy, leading to semantic misalignment and structural hallucinations. To address this, we decompose CT synthesis into semantics-structure alignment through the proposed Semantics and Structure Loss. Specifically, the Semantic Intensity Distribution (SID) Loss partitions the broad Hounsfield Unit (HU) range into semantic subspaces to achieve tissue-specific intensity alignment. Building on SID-based semantic alignment, the Structural Anatomy (SA) Loss further improves structural fidelity by regularizing gradient manifolds within clinically-informed spatial domains. Extensive experiments across GAN, Diffusion, Flow, and foundation models demonstrate the broad compatibility and consistent gains of our approach. A user study further confirms the clinical realism and mask fidelity, while downstream segmentation demonstrates the practical utility. Code will be released after acceptance.