Abstract
Knowledge of input blood to the brain, which is represented as total cerebral blood flow (tCBF), is important in evaluating brain health. Phase-contrast (PC) magnetic resonance imaging (MRI) enables blood velocity mapping, allowing for noninvasive measurements of tCBF. In the procedure, manual selection of brain-feeding arteries is an essential step, but is time-consuming and often subjective. Thus, the purpose of this work was to develop and validate a deep learning (DL)-based technique for automated tCBF quantifications. To enhance the DL segmentation performance on arterial blood vessels, in the preprocessing step magnitude and phase images of PC MRI were multiplied several times. Thereafter, a U-Net was trained on 218 images for three-class segmentation. Network performance was evaluated in terms of the Dice coefficient and the intersection-over-union (IoU) on 40 test images, and additionally, on externally acquired 20 datasets. Finally, tCBF was calculated from the DL-predicted vessel segmentation maps, and its accuracy was statistically assessed with the correlation of determination (R(2)), the intraclass correlation coefficient (ICC), paired t-tests, and Bland-Altman analysis, in comparison to manually derived values. Overall, the DL segmentation network provided accurate labeling of arterial blood vessels for both internal (Dice=0.92, IoU=0.86) and external (Dice=0.90, IoU=0.82) tests. Furthermore, statistical analyses for tCBF estimates revealed good agreement between automated versus manual quantifications in both internal (R(2)=0.85, ICC=0.91, p=0.52) and external (R(2)=0.88, ICC=0.93, p=0.88) test groups. The results suggest feasibility of a simple and automated protocol for quantifying tCBF from neck PC MRI and deep learning.