#40: QC-Studio-integrated quality control toolkit for MRI datasets

MRI quality control (QC) is an essential but highly manual and repetitive process in neuroimaging research. Currently, reviewers must navigate scattered directories and mentally integrate diverse outputs, such as 3D volumes, SVG montages, and isolated metrics, to make a reliable assessment. This "hidden labor" takes significant time away from actual research, introduces variability across different raters, and results in decisions that are poorly documented and difficult to audit. This project aims to transform QC-Studio from a working prototype into a robust, Nipoppy-integrated application. The core of the solution focuses on replacing hardcoded file paths with a scalable, config-driven architecture utilizing Nipoppy's DatasetLayout API. Additionally, the project will inject essential quantitative context into the review process by building an Image Quality Metric (IQM) distribution panel. Finally, it will establish a structured "Evidence Bundle" to experimentally evaluate whether a lightweight LLM-based assistant can improve review efficiency by summarizing quantitative data and flagging anomalies. Key Deliverables: 1) Config-Driven Architecture: A unified Streamlit application featuring dynamic subject loading, unified pagination, and manifest.tsv integration across multiple processing pipelines without hardcoded paths. 2) Quantitative Evidence Layer: An interactive IQM panel that visualizes subject-level metrics against local datasets and crowdsourced reference distributions (MRIQC Web API), complete with rule-based outlier detection. 3) Experimental AI Guidance Prototype: A foundational integration displaying pre-generated, LLM-authored summary reports within the dashboard, accompanied by an evaluation report assessing factual accuracy and workflow impact.