ASL 4D MRA Intracranial Vessel Segmentation With Deep Learning U-Nets.

ABSTRACT:

Purpose

To propose a spatio-temporal U-Net based network (4DST) that exploits both spatial and dynamic information while avoiding memory-intensive 4D convolutional layers for ASL-based non-contrast enhanced 4-dimensional MR angiography (4D MRA) vessel segmentation.

Methods

Pulsed ASL-based 4D MRA data were collected on 35 healthy volunteers and 5 arteriovenous malformation patients. Spatial only (2D, 3D) and spatio-temporal U-Net variations (including the proposed 4DST) were tested. Two recently developed methods, including feature-based isolation forest and BRAVE-Net, were used for comparison. Dice-Sørensen coefficient (DSC), center-line Dice (clDice), Hausdorff distance (HD), precision, accuracy, specificity, and sensitivity were calculated. Sensitivity was analyzed relati

SUBMITTER: Chung SH 

PROVIDER: S-EPMC12850589 | biostudies-literature | 2026 Apr

REPOSITORIES: biostudies-literature