Abstract
Among the methods of parallel magnetic resonance imaging (PMRI), Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) technique reconstructs the missing k-space data by a set of weights, which are derived from auto-calibration signal (ACS) lines acquired in parallel to the reduced lines. In this paper, a novel hybrid method is proposed to reconstruct by cross sampling the ACS lines orthogonal to the reduced lines and estimating weights with a second-order nonlinear model. The proposed method can mix the benefits of cross sampling and the nonlinear kernel model. The in vivo experiments demonstrate this method, named as cross-sampled nonlinear (CSNL) GRAPPA, can effectively reduce the aliasing artifacts and noises when high acceleration is desired.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call