Beyond Isolated Scans: Cross-Phase Alignment of Structure and Topology for 3D Medical Pretraining

Self-supervised learning (SSL) has significantly advanced 3Dmedical image analysis. However, existing pretraining methods typicallytreat multi-phase scans as isolated samples, neglecting the rich anatom-ical correspondence inherent in paired acquisitions. Clinically, contrast-enhanced CT (CECT) offers a structure-amplified view of the exactanatomy captured in a paired non-contrast CT (NCCT), yet the drasticintensity shifts caused by contrast agents make direct voxel-wise align-ment challenging. To harness this naturally occurring supervisory signalwhile decoupling structural topology from phase-specific appearance, wepropose CAST (Cross-phase Alignment of Structure and Topology),a novel paired pretraining framework. CAST employs a 3D CNN archi-tecture to explicitly align NCCT representations with CECT targets.Moving beyond conventional reconstruction, we introduce two feature-level constraints: (1) a Spectral Consistency module that utilizes3D wavelet decomposition to align frequency-aware boundaries whilesuppressing contrast-induced noise; (2) a Geometry-Aware Topo-logical Consistency module that preserves local relational graphsamong salient anatomical keypoints via dynamic top-hat sampling. Tosupport this, we construct a large-scale dataset comprising 13,850 pairedvolumetric CT scans. Extensive evaluations on public benchmarks andreal-world clinical cohorts demonstrate that CAST achieves state-of-the-art performance. Notably, our method delivers substantial gains inboundary-sensitive segmentation tasks and low-data regimes, proving itsefficacy in learning robust, phase-invariant anatomical representationsfor both NCCT and CECT downstream applications. Code is availableat https://github.com/alibaba-damo-academy/CAST.