发表机构
University of Science and Technology of China; Technical University of Munich（中国科学技术大学; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对CT诊断阶段选择协议差异及现有方法局限，提出策略驱动的CT智能体（PD-CTAgent）。通过临床结构抽象模块和知识引导诊断控制模型，实现阶段感知诊断推理，实验验证其在多数据集上的有效性与临床一致性。
AI 中文摘要
计算机断层扫描（CT）诊断通常基于初步发现、临床怀疑和诊断指南动态选择成像阶段，如非增强、动脉或静脉期。这种逐阶段决策过程对于减少不必要的辐射暴露、支持及时分期和治疗规划至关重要。然而，不同医院、地区和指南的阶段选择协议可能不同，现有基于CT的人工智能方法大多假设所有阶段都可用，且专注于固定成像阶段下的静态任务，无法对是否需要额外阶段进行建模。为应对这些挑战，我们提出了策略驱动的CT智能体（PD-CTAgent）用于临床一致的CT阶段选择和诊断推理。PD-CTAgent引入了临床结构抽象模块（CSAM）将异构CT阶段统一为具有阶段感知的证据表示。基于此表示，知识引导诊断控制模型（KDCM）评估阶段充足性并在必要时迭代请求额外阶段。策略驱动的智能体设计使PD-CTAgent能够灵活遵循不同机构、地区或指南特定的诊断协议。在两个公共数据集LIDC和MCT-LTDiag以及一个私有数据集上的实验证明了其有效性和临床一致性。代码将在接受后公开。
英文摘要
Computed Tomography (CT) diagnosis often relies on dynamic selection of imaging phases, such as non-contrast, arterial, or venous phases, based on preliminary findings, clinical suspicion, and diagnostic guidelines. This phase-wise decision process is critical for reducing unnecessary radiation exposure while supporting timely staging and treatment planning. However, phase-selection protocols can vary across hospitals, regions, and guidelines, while most existing CT-based AI methods assume that all phases are available and focus on static tasks under a fixed imaging phase, failing to model whether additional phases are required. This limitation stems from heterogeneous multi-phase representations, the need for knowledge-guided phase control beyond visual cues, and the lack of supervision for phase-sufficiency decisions in existing datasets. To address these challenges, we propose Policy-Driven CT-Agent (PD-CTAgent) for clinically consistent CT phase selection and diagnostic reasoning. PD-CTAgent introduces a Clinical Structure Abstraction Module (CSAM) to harmonize heterogeneous CT phases into a unified, phase-aware evidence representation. Based on this representation, a Knowledge-Guided Diagnostic Control Model (KDCM) evaluates phase sufficiency and iteratively requests additional phases when necessary. The policy-driven agent design further allows PD-CTAgent to flexibly follow different institutional, regional, or guideline-specific diagnostic protocols. Together, PD-CTAgent bridges static CT analysis and real-world clinical workflows. Experiments on two public datasets, LIDC and MCT-LTDiag, and one private dataset demonstrate its effectiveness and clinical consistency. Code will be made public upon acceptance.
Comments8 pages, 4 figures
