Prior-driven multimodal contrastive learning with Fourier cross-attention to detect aortic dissection in 3D non-contrast CT.

Accurate identification of aortic dissection (AD) in emergencies is clinically critical. Non-contrast CT (NC-CT), the standard emergency imaging for chest pain, has limited AD diagnostic sensitivity. We developed an anatomy prior-driven multimodal contrastive learning framework, extracting discriminative features from contrast-enhanced CT (CE-CT) and radiological reports to enhance NC-CT-based AD detection. Aortic segmentation and straightening focus analysis on relevant anatomy, while our tri-encoder architecture leverages Fourier cross-attention (core innovation) to co-extract spatial and frequency-domain features with hybrid contrastive-focal frequency loss. After pre-training, only NC-CT is needed for inference. Evaluated on 134 subjects (70 AD, 64 non-AD), our method achieved 0.955 accuracy and 0.983 AUC, outperforming 9 SOTA models. External validation showed 0.933 accuracy, with lumen segmentation reaching an average Dice score of 0.773. These results demonstrate that our method can advance AD detection and enable accurate true/false lumen localization using NC-CT alone.