A Fully Automated Analysis Pipeline for 4D Flow MRI in the Aorta

作者：Ethan Johnson, Haben Berhane, Elizabeth Weiss, Kelly Jarvis, Aparna Sodhi, Kai Yang, Joshua D. Robinson, Cynthia K. Rigsby, Bradley D. Allen, Michael Markl · 发表于：Bioengineering · 年份：2025 · DOI：10.3390/bioengineering12080807 · 被引用次数：2 · 研究领域：Cardiac Valve Diseases and Treatments、Advanced MRI Techniques and Applications、Cardiovascular Function and Risk Factors

Four-dimensional (4D) flow MRI has shown promise for the assessment of aortic hemodynamics. However, data analysis traditionally requires manual and time-consuming human input at several stages. This limits reproducibility and affects analysis workflows, such that large-cohort 4D flow studies are lacking. Here, a fully automated artificial intelligence (AI) 4D flow analysis pipeline was developed and evaluated in a cohort of over 350 subjects. The 4D flow MRI analysis pipeline integrated a series of previously developed and validated deep learning networks, which replaced traditionally manual processing tasks (background-phase correction, noise masking, velocity anti-aliasing, aorta 3D segmentation). Hemodynamic parameters (global aortic pulse wave velocity (PWV), peak velocity, flow energetics) were automatically quantified. The pipeline was evaluated in a heterogeneous single-center cohort of 379 subjects (age = 43.5 ± 18.6 years, 118 female) who underwent 4D flow MRI of the thoracic aorta (n = 147 healthy controls, n = 147 patients with a bicuspid aortic valve [BAV], n = 10 with mechanical valve prostheses, n = 75 pediatric patients with hereditary aortic disease). Pipeline performance with BAV and control data was evaluated by comparing to manual analysis performed by two human observers. A fully automated 4D flow pipeline analysis was successfully performed in 365 of 379 patients (96%). Pipeline-based quantification of aortic hemodynamics was closely correlated with manu...