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Abstract #1216

Evaluating Segmentation Techniques for Circle of Willis in 4D Flow MRI: A Comparative Study

Jiaxin Zhang1, Anouk Verschuur1, Eric Schrauben2, Mark Bakker3, Pim van Ooij2, Kyung Min Nam4, Irene C. van der Schaaf4, and Chantal M.W. Tax1,5
1Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, 2Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, 3Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, Netherlands, 4Deparment of Radiology, University Medical Center Utrecht, Utrecht, Netherlands, 5CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom

Synopsis

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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Keywords