NeuroPilot：一种用于神经影像处理、质量控制与管理的智能体驱动智能流程
NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages
- University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出NeuroPilot多智能体系统，整合神经影像处理、质控与管理流程，在17个超12.3万受试者的队列中验证，将传统2-3个月的工作压缩至一周，提升了可扩展性与效率。
AI中文摘要：
将原始神经影像档案转化为可用于分析的衍生数据依赖于三个脆弱的阶段：数据标准化、模态特异性预处理和质量控制（QC）。虽然单个神经影像工具已发展得较为成熟，但它们的编排需要特定项目的脚本、适应环境的调整以及费力的手动质量控制。为解决这一问题，我们提出了NeuroPilot，这是一个多智能体系统，它将神经影像处理、质量控制和数据管理的专业知识数字化为三种可调用大语言模型（LLM）的技能：dcm2bids-skill、neuroimage-pre-skill和qc-agent-skill。由LLM驱动的智能体自主编排工作流程，将各种基础设施设置统一为单一配置，以实现最高的可扩展性。为展示该系统的通用性，我们在17个队列（超过123000名受试者）中部署了NeuroPilot，涵盖从婴儿到老年人群以及多种MRI模态（结构、扩散、功能）。在实际应用中，通过dcm2bids-skill标准化数据后，智能体会根据可用模态和队列特征将数据集动态路由到最优的neuroimage-pre-skill（例如，将T1w和fMRI数据分配给fMRIPrep，或为婴儿队列选择专用流程）。随后，qc-agent-skill通过3D浏览器仪表盘驱动基于证据的半自动化质量控制，利用多层验证系统优化不合格案例并将复杂问题升级给主管检查。定量而言，我们的质量控制智能体筛选了558名生产受试者，验证其自动标记与FreeSurfer的拓扑缺陷指标的一致性。婴儿处理流程在经质量控制验证的输入上实现了100%（201/201）的完成率。重要的是，NeuroPilot将培训人员和处理完整数据集的传统2-3个月时间压缩至仅一周。NeuroPilot已部署在此https URL。
英文摘要：
Transforming raw neuroimage archives into analysis-ready derivatives relies on three brittle stages: data standardization, modality-specific preprocessing, and quality control (QC). While individual neuroimaging tools are well developed, their orchestration requires project-specific scripts, environment-adaptive tuning, and labor-intensive manual QC. To address this, we introduce NeuroPilot, a multi-agent system that digitalizes the expertise of neuroimage processing, QC, and data management into three LLM-invocable skills: dcm2bids-skill, neuroimage-pre-skill, and qc-agent-skill. The LLM-driven agent autonomously orchestrates workflows, generalizing various infrastructure settings into a single configuration to achieve the highest scalability. Demonstrating the system's generalizability, we deployed NeuroPilot across 17 cohorts (>123,000 subjects) spanning infant to aging populations and multiple MRI modalities (structural, diffusion, functional). In practice, after standardizing data via the dcm2bids-skill, the agent dynamically routes datasets to the optimal neuroimage-pre-skill based on available modalities and cohort traits (e.g., dispatching T1w and fMRI data to fMRIPrep, or selecting specialized pipelines for infant cohorts). The qc-agent-skill then drives an evidence-based, semi-automated QC via a 3-D browser dashboard, utilizing a multi-tiered verification system to optimize failed cases and escalate complex issues for supervisor inspection. Quantitatively, our QC agent screened 558 production subjects, validating its automated flags against FreeSurfer's topology-defect metrics. The infant processing pipeline achieved a 100% (201/201) completion rate on QC-validated inputs. Importantly, NeuroPilot compresses the traditional 2--3 month timeline for training staff and processing complete datasets into a single week. NeuroPilot is deployed in https://wanda-cyberbench.com/.
