Abstract
AbstractCerebral artery segmentation plays an important role in the direct visualization of the human brain to obtain vascular system information. At ultra-high field magnetic resonance imaging, hyperintensity of the cerebral arteries in 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 echoes (MP2RAGE) sequence at 7 T. The proposed method employed a seed-based region-growing strategy with the following procedures. (1) Two seed regions were defined by Frangi filtering applied to T1w images and by a simple calculation from multi-contrast images, (2) the two seed regions were combined, (3) the combined seed regions were expanded using a region growing algorithm to acquire the cerebral arteries. Time-of-flight (TOF) images were obtained as a reference to evaluate the proposed method. We successfully performed vessel segmentations from T1w MP2RAGE images, which mostly overlapped with the segmentations from the TOF images. As large arteries can affect the normalization of anatomical images to the standard coordinate space in functional and structural studies, we also investigated the effect of the cerebral arteries on spatial transformation using vessel segmentation by the proposed method. As a result, the T1w image removing the cerebral arteries showed better agreement with the standard atlas compared with the T1w image containing the arteries. Thus, because the proposed method using MP2RAGE images can obtain brain tissue anatomical information as well as cerebral artery information without need for additional acquisitions such as of the TOF sequence, it is useful and time saving for medical diagnosis and functional and structural studies.
Publisher
Cold Spring Harbor Laboratory