Keywords: Blood Vessels, Velocity & Flow
Motivation: 7T 4D flow magnetic resonance imaging (MRI) has enhanced the visualization and quantification of flow within cerebral arteries. However, differences in vessel segmentation can cause variability in flow quantification.
Goal(s): This study aims to develop and validate automatic artery segmentation for 4D flow MRI, especially focusing on Circle of Willis (CoW).
Approach: We compared the segmentation performance and flow measurements of two deep learning (DL) models (3D U-Net and nnUNet) a thresholding algorithm from QVT software with those of manual segmentation.
Results: nnUNet demonstrates the best segmentation performance in extracting small vessels and QVT resulted in the highest flow estimates.
Impact: Accurate automatic intracranial vessel segmentation methods decrease the need for manual intervention and facilitate the measurement of flow in smaller arteries.
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