Motion-corrected 4D-Flow MRI for neurovascular applications

Neurovascular 4D-Flow MRI has emerged as a powerful tool for comprehensive cerebrovascular hemodynamic characterization. Clinical studies in at risk populations such as aging adults indicate hemodynamic markers can be confounded by motion-induced bias. This study develops and characterizes a high fidelity 3D self-navigation approach for retrospective rigid motion correction of neurovascular 4D-Flow data. A 3D radial trajectory with pseudorandom ordering was combined with a multi-resolution low rank regularization approach to enable high spatiotemporal resolution self-navigators from extremely undersampled data. Phantom and volunteer experi-ments were performed at 3.0T to evaluate the ability to correct for different amounts of induced motions. In addition, the approach was applied to clinical-research exams from ongoing aging studies to characterize perfor-mance in the clinical setting. Simulations, phantom and volunteer experiments with motion correction produced images with increased vessel conspicuity, reduced image blurring, and decreased variability in quantitative mea-sures. Clinical exams revealed significant changes in hemodynamic parameters including blood flow rates, flow pulsatility index, and lumen areas after motion correction in probed cerebral arteries (Flow: P < 0.001 Lt ICA, P = 0.002 Rt ICA, P = 0.004 Lt MCA, P = 0.004 Rt MCA; Area: P < 0.001 Lt ICA, P < 0.001 Rt ICA, P = 0.004 Lt MCA, P = 0.004 Rt MCA; flow pulsatility index: P = 0.042 Rt ICA, P = 0.002 Lt MCA). Motion induced bias can lead to significant overestimation of hemodynamic markers in cerebral arteries. The proposed method reduces measure-ment bias from rigid motion in neurovascular 4D-Flow MRI in challenging populations such as aging adults.

Automated summary

Neurovascular 4D-Flow MRI is a powerful tool for cerebrovascular hemodynamic characterization. However, motion-induced bias can confound the hemodynamic markers. This study develops a high fidelity 3D self-navigation approach for motion correction and demonstrates improved image quality and reduced measurement bias.