Total-body PET/CT enables system-wide molecular imaging, but heterogeneous anatomical and metabolic signals, approximately 2 m axial coverage, and structured radiology semantics challenge existing medical AI models that assume single-modality inputs, localized fields of view, and coarse image-text alignment. We introduce SDF-HOLO (Systemic Dual-stream Fusion Holo Model), a multimodal foundation model for holistic total-body PET/CT, pre-trained on more than 10,000 patients. SDF-HOLO decouples CT and PET representation learning with dual-stream encoders and couples them through a cross-modal interaction module, allowing anatomical context to refine PET aggregation while metabolic saliency guides subtle morphological reasoning. To model long-range dependencies across the body, hierarchical context modeling combines efficient local windows with global attention. To bridge voxels and clinical language, we use anatomical segmentation masks as explicit semantic anchors and perform voxel-mask-text alignment during pre-training. Across tumor segmentation, low-dose lesion detection, and multilingual diagnostic report generation, SDF-HOLO outperforms strong task-specific and clinical-reference baselines while reducing localization errors and hallucinated findings. Beyond focal interpretation, the model enables system-wide metabolic profiling and reveals tumor-associated fingerprints of inter-organ metabolic network interactions, providing a scalable computational foundation for total-body PET/CT diagnostics and system-level precision oncology.

翻译：全身PET/CT虽能实现系统级分子成像，但其异构的解剖与代谢信号、约2米的轴向覆盖范围以及结构化的放射学语义，对现有医疗AI模型构成了挑战——这些模型通常假设单模态输入、局部视野及粗略的图文对齐。本文提出SDF-HOLO（系统性双流融合全息模型），这是一个面向全身PET/CT整体分析的多模态基础模型，基于超过10,000例患者数据进行预训练。SDF-HOLO通过双流编码器解耦CT与PET表征学习，并借助跨模态交互模块实现耦合，使解剖上下文能优化PET特征聚合，同时代谢显著性可引导精细的形态学推理。为建模全身长程依赖关系，分层上下文建模将高效局部窗口与全局注意力机制相结合。为弥合体素与临床语言之间的鸿沟，我们使用解剖分割掩码作为显式语义锚点，并在预训练阶段执行体素-掩码-文本对齐。在肿瘤分割、低剂量病灶检测及多语言诊断报告生成等任务中，SDF-HOLO均优于强任务专用模型及临床参考基线，同时减少了定位误差与幻觉性发现。除局部病灶解析外，该模型还能实现系统级代谢谱分析，揭示肿瘤相关的跨器官代谢网络交互特征图谱，为全身PET/CT诊断及系统级精准肿瘤学提供了可扩展的计算基础。