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Abstract #2749

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

Edward John Clarkson1,2, Haribalan Kumar3, Jet Wright1,2, Itamar Terem4, Eryn Kwon1,2, and Samantha J. Holdsworth1,5
1Mātai Medial Research Institue, Tairāwhiti Gisborne, New Zealand, 2Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, 3General Electric Healthcare, Tairāwhiti Gisborne, New Zealand, 4Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 5Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland, Auckland, New Zealand

Synopsis

Keywords: Data Processing, Analysis/Processing, Quantitative Imaging, Data Processing

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.

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Keywords