Improve GRAPPA with Cross-sampled ACS Lines and Nonlinear Kernel Model
Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 2nd International Conference on Biomedical Engineering and Biotechnology, 11–13 October 2013, Wuhan, China
Affiliations: Department of Educational Technology, Yuxi Normal University, Yunnan, China | Department of Diagnostic Radiology, Yale University, New Haven, CT, USA | Department of Physics, Yuxi Normal University, Yunnan, China
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.
