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Abstract #4279

Automated Deep Learning Pipeline for Pulse Wave Velocity Measurement in UK Biobank MRI Data

Yue Jiang1, Tina Yao1, Karan Punjabi2, Daniel Knight3, Jennifer Steeden1, Rhodri Davies1, and Vivek Muthurangu1
1University College London, London, United Kingdom, 2Barts Health Center, London, United Kingdom, 3Royal Free Hospital, London, United Kingdom

Synopsis

Keywords: Analysis/Processing, Cardiovascular, Pulse wave velocity, UK Biobank, localizer images, arterial stiffness

Motivation: MRI-based pulse wave velocity (PWV) is a golden marker of arterial stiffness, but the acquisition and manual segmentation are time-intensive. This study introduces an automated pipeline designed to accelerate PWV measurement process.

Goal(s): We aim to present an automated pipeline for measuring aortic arch PWV from MRI localizers and phase contrast MR (PCMR).

Approach: We trained two deep learning models: one to generate 3D aorta segmentations from 2D localizers for accurate 3D aortic length measurement, and another to segment PCMR for flow curves and transit time calculation.

Results: Our model proved to be able to produce automated PWV measurement on UK Biobank dataset.

Impact: Our model enables automatic aortic arch PWV measurement for UK Biobank subjects, which can be used in the investigation of arterial stiffness and prediction of cardiovascular disease for a large population.

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Keywords