Computed Tomography (CT) is one of the most widely used and diagnostically information-dense imaging modalities, covering critical organs such as the heart, lungs, liver, and colon. Clinical interpretation relies on both slice-driven local features (e.g., sub-centimeter nodules, lesion boundaries) and volume-driven spatial representations (e.g., tumor infiltration, inter-organ anatomical relations). However, existing Large Vision-Language Models (LVLMs) remain fragmented in CT slice versus volumetric understanding: slice-driven LVLMs show strong generalization but lack cross-slice spatial consistency, while volume-driven LVLMs explicitly capture volumetric semantics but suffer from coarse granularity and poor compatibility with slice inputs. The absence of a unified modeling paradigm constitutes a major bottleneck for the clinical translation of medical LVLMs. We present OmniCT, a powerful unified slice-volume LVLM for CT scenarios, which makes three contributions: (i) Spatial Consistency Enhancement (SCE): volumetric slice composition combined with tri-axial positional embedding that introduces volumetric consistency, and an MoE hybrid projection enables efficient slice-volume adaptation; (ii) Organ-level Semantic Enhancement (OSE): segmentation and ROI localization explicitly align anatomical regions, emphasizing lesion- and organ-level semantics; (iii) MedEval-CT: the largest slice-volume CT dataset and hybrid benchmark integrates comprehensive metrics for unified evaluation. OmniCT consistently outperforms existing methods with a substantial margin across diverse clinical tasks and satisfies both micro-level detail sensitivity and macro-level spatial reasoning. More importantly, it establishes a new paradigm for cross-modal medical imaging understanding.

翻译：计算机断层扫描（CT）是最广泛应用且诊断信息最密集的成像模态之一，涵盖心脏、肺、肝脏和结肠等关键器官。临床解读既依赖于切片驱动的局部特征（如亚厘米结节、病灶边界），也依赖于体积驱动的空间表征（如肿瘤浸润、器官间解剖关系）。然而，现有极大视觉语言模型（LVLM）在CT切片与体积理解方面仍处于割裂状态：切片驱动的LVLM展现出强泛化能力但缺乏跨切片空间一致性，而体积驱动的LVLM虽能显式捕捉体积语义，却存在粒度粗糙、与切片输入兼容性差的问题。统一建模范式的缺失构成了医学LVLM临床转化的主要瓶颈。本文提出OmniCT，一种面向CT场景的强大统一切片-体积LVLM，其贡献包括：（i）空间一致性增强（SCE）：通过体积切片组合与三轴位置编码引入体积一致性，并采用混合专家（MoE）混合投影实现高效的切片-体积适配；（ii）器官级语义增强（OSE）：通过分割与感兴趣区域定位显式对齐解剖区域，强化病灶级与器官级语义；（iii）MedEval-CT：最大规模的切片-体积CT数据集与混合基准，整合了统一评估的综合指标。OmniCT在多样化临床任务中均以显著优势超越现有方法，同时满足微观细节敏感性与宏观空间推理需求。更重要的是，它为跨模态医学影像理解建立了新范式。