Vector flow imaging is a critical component in the clinical diagnosis of cardiovascular diseases; however, most current methods are too computationally expensive to scale well to 3D. Less complex techniques, such as Doppler-based imaging (which cannot provide lateral flow measurements) and basic speckle tracking algorithms (which have poor lateral accuracy), are incapable of producing high quality 3D measurements. In this paper, we first extend a technique designed to improve lateral flow accuracy for 2D velocity vector estimation, the synthetic lateral phase method, to 3D (SLP-3D). We then show that a straightforward implementation of this algorithm is too computationally complex for modern systems. Instead, we propose a two-tiered method that uses low complexity sum-of-absolute differences (SAD) for coarse-grained search and an optimized version of SLP-3D to fine tune the search for sub-pixel accuracy. We show that the proposed method (SAD+SLP-3Dopt) achieves a 9× reduction in computational complexity compared to the naive SLP-3D. Field II simulations for plug and parabolic flow using our method show a fairly high degree of accuracy in both the axial and the lateral components. Finally, we show our technique can support accurate flow imaging with up to 130 velocity estimations/sec within the power constraints of a handheld device.