Artifact-robust vascular segmentation for 3D phase-contrast MR angiography using a deep learning approach

We developed a segmentation algorithm for PC-MRA using a deep-learning approach, with the goal of achieving artifact-robust segmentation for PC-MRA. To simulate flow-related artifacts of MRA, Gaussian noise and phase error were added to the k-space domain of the datasets. LadderNet consists of two consecutive U-nets with skip connections, and has been adopted as a training network for vessel segmentation. Retrospective studies demonstrated superior accuracy and precision of the proposed method over a conventional level set segmentation method.