Abstract #0806

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

Daiki Tamada¹, Thekla H Oechtering^1,2, Eisuke Takai³, and Scott B Reeder^1,4,5,6,7

¹Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Department of Radiology, Universität zu Lübeck, Lübeck, Germany, ³MIRAI Technology Institute, Shiseido Co., Ltd., Tokyo, Japan, ⁴Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ⁵Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁶Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁷Emergency, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Segmentation, Segmentation, phase contrast MRAWe 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.

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