Deriving high-resolution velocity maps from low-resolution fourier velocity encoded MRI data

Fourier velocity encoding (FVE) is a promising magnetic resonance imaging (MRI) method for measurement of cardiovascular blood flow. FVE provides considerably higher SNR than phase contrast imaging, and is robust to partial-volume effects. FVE data is usually acquired with low spatial resolution, due to scan-time restrictions associated with its higher dimensionality. Thus, FVE is capable of providing the velocity distribution associated with a large voxel, but does not directly provides a velocity map. Velocity maps, however, are useful for calculating the actual blod flow through a vessel, or for guiding computational fluid dynamics simulations. This work proposes a method to derive velocity maps with high spatial resolution from low-resolution FVE data using a hyper-Laplacian prior deconvolution algorithm. Experiments using numerical phantoms, as well simulated spiral FVE data derived from real phase contrast data, acquired using a pulsatile carotid flow phantom, show that it is possible to obtain reasonably accurate velocities maps from low-resolution FVE distributions.