Automatic Quantification of Total Cerebral Blood Flow from Phase-Contrast MRI Using Deep Learning

In this work, we aim to develop a deep learning (DL)-based processing pipeline that enables rapid and correct segmentation of brain-feeding arteries in neck phase-contrast (PC) MR images, thereby achieving accurate quantification of total cerebral blood flow (tCBF) in an automated manner. To this end, we implemented a U-Net architecture where magnitude/phase-combined PC images are provided for network training. The results suggest that the present, automated method yields accurate measurements of tCBF in comparison to ground truth values obtained from manual vessel segmentation.