Accelerated dual-venc 4D flow MRI for neurovascular applications

PurposeTo improve velocity-to-noise ratio (VNR) and dynamic velocity range of 4D flow magnetic resonance imaging (MRI) by using dual-velocity encoding (dual-venc) with k-t generalized autocalibrating partially parallel acquisition (GRAPPA) acceleration. Materials and MethodsA dual-venc 4D flow MRI sequence with k-t GRAPPA acceleration was developed using a shared reference scan followed by three-directional low- and high-venc scans (repetition time / echo time / flip angle=6.1 msec / 3.4 msec / 15 degrees, temporal/spatial resolution=43.0 msec/1.2 x 1.2 x 1.2mm(3)). The high-venc data were used to correct for aliasing in the low-venc data, resulting in a single dataset with the favorable VNR of the low-venc but without velocity aliasing. The sequence was validated with a 3T MRI scanner in phantom experiments and applied in 16 volunteers to investigate its feasibility for assessing intracranial hemodynamics (net flow and peak velocity) at the major intracranial vessels. In addition, image quality and image noise were assessed in the in vivo acquisitions. ResultsAll 4D flow MRI scans were acquired successfully with an acquisition time of 204 minutes. The shared reference scan reduced the total acquisition time by 12.5% compared to two separate scans. Phantom experiments showed 51.4% reduced noise for dual-venc compared to high-venc and an excellent agreement of velocities (=0.8, P < 0.001). The volunteer data showed decreased noise in dual-venc data (54.6% lower) compared to high-venc, and improved image quality, as graded by two observers: fewer artifacts (P < 0.0001), improved vessel conspicuity (P < 0.0001), and reduced noise (P < 0.0001). ConclusionDual-venc 4D flow MRI exhibits the superior VNR of the low-venc acquisition and reliably incorporates low- and high-velocity fields simultaneously. In vitro and in vivo data demonstrate improved flow visualization, image quality, and image noise. Level of Evidence: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:102-114