Quality Check ToolBox V1.0

Problem: There is currently no standalone, open source Python library within the OSIPI ecosystem for post-processing quality control (QC) of Arterial Spin Labeling (ASL) MRI perfusion data. Existing solutions are either MATLAB dependent, closed-source or tightly coupled to specific preprocessing pipelines, making scalable and reproducible QC difficult for clinical and research datasets. Solution: I will build Quality Check ToolBox V1.0 for osipy a pipeline-agnostic, modular Python library that evaluates ASL-derived Cerebral Blood Flow (CBF) maps and generates interpretable PASS/WARN/FAIL triage metrics. Using a configurable registry architecture, it evaluates the Quality Evaluation Index (QEI) alongside orthogonal checks for BIDS compliance, M0 calibration and motion. It features graceful degradation and population-specific YAML configurations (e.g., pediatric, stroke) to successfully distinguish real pathological signals from acquisition artifacts. Deliverables: Core osipy-integrated QC pipeline with a @register_qc_check module registry. Full implementation of the Dolui et al. 2024 QEI metric. Fallback-capable modules for control-label validation, M0 saturation, and motion tracking (FWD/DVARS). Configurable PASS/WARN/FAIL verdict logic that prevents pipeline crashes on missing data. A comprehensive Pytest suite and MkDocs documentation. A standalone HTML visual dashboard for reviewing QC metrics across cohorts.