Intracranial Arterial Flow Velocimetry in Quantitative Time-of-Flight MR Angiography using Deep Machine Learning

Quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) is a recently introduced technique that provides for simultaneous luminal and hemodynamic imaging of the intracranial arteries. We hypothesized that the application of a deep machine learning (DML) image analysis strategy to qTOF MRA data would improve agreement of intracranial arterial velocity measures with respect to phase contrast MRI. Compared to a more conventional image analysis procedure, we found that the application of DML image analysis to qTOF data improved agreement of component, total, and peak intracranial arterial flow velocity measures with respect to phase contrast MRI, and reduced calculation times by 35-fold.