An automated q-aMRI pipeline for scalable brain displacement analysis: Enhancing speed, consistency, and clinical relevance

Motivation: Centralized processing pipelines ensure consistent output conventions and facilitate fast efficient processing. A standardized and flexible automated q-aMRI processing pipeline does not currently exist. Goal(s): This work introduces an automated pipeline tailored to the q-aMRI algorithm, minimizing manual intervention to improve throughput and enabling widespread deployment for brain displacement analysis. Approach: Cardiac-gated balanced steady-state free precession (bSSFP) scans from various sources are organized into a BIDS directory. Each scan undergoes brain extraction, segmentation, and displacement mapping, with results visualized and statistically analyzed across groups. Results: The pipeline facilitated automated analysis of two extensive clinical datasets, providing visualization, processing, and standard data exporting. Impact: Large-scale quantification is essential to uncover the clinical significance of brain displacement in disease contexts. This automated q-aMRI pipeline streamlines the analysis process, enhancing the speed and scalability of displacement analyses in clinical cohorts.