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.
Zhang et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: