Artifact-robust Deep Learning-based Segmentation of 3D Phase-contrast MR Angiography: A Novel Data Augmentation Approach

Abstract

This study presents a novel data augmentation approach to improve deep learning (DL)-based segmentation for 3D phase-contrast magnetic resonance angiography (PC-MRA) images affected by pulsation artifacts. Augmentation was achieved by simulating pulsation artifacts through the addition of periodic errors in k-space magnitude. The approach was evaluated on PC-MRA datasets from 16 volunteers, comparing DL segmentation with and without pulsation artifact augmentation to a level-set algorithm. Results demonstrate that DL methods significantly outperform the level-set approach and that pulsation artifact augmentation further improves segmentation accuracy, especially for images with lower velocity encoding. Quantitative analysis using Dice-Sørensen coefficient, Intersection over Union, and Average Symmetric Surface Distance metrics confirms the effectiveness of the proposed method. This technique shows promise for enhancing vascular segmentation in various anatomical regions affected by pulsation artifacts, potentially improving clinical applications of PC-MRA.