title: Improve GRAPPA with cross-sampled ACS lines and nonlinear kernel model.
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.
authors.affiliation: Department of Educational Technology, Yuxi Normal University, Yunnan, China.
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Wang
authors.forename: Haifeng
authors.initials: H
authors.lastname: Wang
authors.forename: Jing
authors.initials: J
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D002138:Calibration; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D017711:Nonlinear Dynamics; D019047:Phantoms, Imaging
keywords: GRAPPA; cross-sampled; nonlinear; parallel MRI