Cerebral artery segmentation based on magnetization-prepared two rapid acquisition gradient echo multi-contrast images in 7 Tesla magnetic resonance imaging.

Cerebral artery segmentation plays an important role in the direct visualization of the human brain to obtain vascular system information. On ultra-high field magnetic resonance imaging, cerebral arteries appearing hyperintense on T1 weighted (T1w) images could be segmented from brain tissues such as gray and white matter. In this study, we propose an automated method to segment the cerebral arteries using multi-contrast images including T1w images of a magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) sequence at 7 T. The proposed method, termed MP2rase-CA (MP2rage based RApid SEgmentation Cerebral Artery), employed a seed-based region-growing strategy and Frangi filtering as well as our brain tissue segmentation (MP2rase Brain Tissue). Time-of-flight (TOF) magnetic resonance angiography (MRA) images were obtained as a reference to evaluate the MP2rase-CA. We successfully performed vessel segmentations, from T1w MP2RAGE images, which mostly overlapped with the segmentations of large cerebral arteries from the TOF-MRA. We also investigated the effect of the large cerebral arteries on spatial transformation of anatomical images to standard coordinate space using vessel segmentation by MP2rase-CA. As a result, the T1w image without the cerebral arteries by MP2rase-CA showed better agreement with the standard atlas compared with the T1w image containing the arteries. In addition, voxel-based morphology showed significant differences between T1w images with and without cerebral arteries in brain areas nearby large arteries. Thus, because MP2rase-CA using MP2RAGE images can obtain brain tissue anatomical information as well as relatively large cerebral artery information without need for additional structure acquisition, it is useful and time saving for functional and structural studies.
Competing Interests: Declaration of Competing Interest HK was employed by Siemens Healthcare K.K. Siemens Healthcare K.K. did not have any additional role in the study design, data collection and analysis. UC and IK have no conflicts of interest.
(Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)