用大模型自动配置CT扫描参数，提升效率减轻技术员负担。
Scan-do Attitude: Towards Autonomous CT Protocol Management using a Large Language Model Agent
- 大模型理解自然语言指令，自动匹配并修改扫描协议
- 实验验证能准确生成适配设备的协议文件，执行请求成功率高
- 适合放射科技术员和设备开发团队参考，推动智能流程落地
在计算机断层扫描（CT）中，根据患者情况调整扫描参数、配置重建方式及选择后处理工具，需兼具临床与技术知识，耗时且依赖专业人员。当前放射科面临技术人员短缺问题。为此，本文提出基于大语言模型（LLM）的智能代理框架，可解析自然语言或结构化、设备无关的协议请求，实现自动化协议配置，旨在提升工作效率、减轻技术员负担。该代理融合上下文学习、指令遵循与结构化工具调用能力，精准识别相关协议要素并实施准确修改。系统评估显示，该方法能有效检索协议组件，生成设备兼容的协议定义文件，并忠实执行用户请求。尽管在原理上可行，仍受限于缺乏统一设备API，存在语法与语义有效性问题，且对模糊或复杂请求处理挑战较大。研究结果表明，基于大模型的代理为实现CT扫描协议的智能化管理提供了明确路径。
原文摘要 · Abstract (English)
Managing scan protocols in Computed Tomography (CT), which includes adjusting acquisition parameters or configuring reconstructions, as well as selecting postprocessing tools in a patient-specific manner, is time-consuming and requires clinical as well as technical expertise. At the same time, we observe an increasing shortage of skilled workforce in radiology. To address this issue, a Large Language Model (LLM)-based agent framework is proposed to assist with the interpretation and execution of protocol configuration requests given in natural language or a structured, device-independent format, aiming to improve the workflow efficiency and reduce technologists' workload. The agent combines in-context-learning, instruction-following, and structured toolcalling abilities to identify relevant protocol elements and apply accurate modifications. In a systematic evaluation, experimental results indicate that the agent can effectively retrieve protocol components, generate device compatible protocol definition files, and faithfully implement user requests. Despite demonstrating feasibility in principle, the approach faces limitations regarding syntactic and semantic validity due to lack of a unified device API, and challenges with ambiguous or complex requests. In summary, the findings show a clear path towards LLM-based agents for supporting scan protocol management in CT imaging.
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