Improving Reproducibility in Quantitative 4D Flow MRI Using AI-Driven Fully-Automated Processing and Analysis

Motivation: Reproducibility is fundamentally an issue for quantitative MRI, and any human intervention required for processing can be a significant source of variability. Goal(s): This study aims to improve reproducibility in quantitative 4D flow MRI by removing all human input from processing, using AI-driven tools. Approach: Hemodynamic parameters quantified by a fully automated neural-network-based processing tool for 4D flow MRI were compared to quantifications performed by two sets of human observers. Results: Moderate but appreciable limits of agreement were observed between quantifications performed by different human observers. Quantified values from fully-automated processing were comparable to those from humans, but all inter-observer variability was eliminated. Impact: This study offers a stable baseline for improving measurement reliability in quantitative 4D flow MRI by removing all manual human inputs required.