NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages

Researchers introduced NeuroPilot, a multi-agent system that automates neuroimage processing, quality control, and data management, achieving a 100% (201/201) completion rate on QC-validated infant inputs and compressing the traditional 2–3 month timeline into one week. Deployed across 17 cohorts (>123,000 subjects), the LLM-driven agent orchestrates workflows via three skills—dcm2bids-skill, neuroimage-pre-skill, and qc-agent-skill—and is available at https://wanda-cyberbench.com/.

arXiv:2608.07541v1 Announce Type: new Abstract: 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/.