title: Cross-sampled GRAPPA for parallel MRI.
abstract: As one widely-used parallel-imaging method, Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) technique reconstructs the missing k-space data by a linear combination of the acquired data using a set of weights. These weights are usually derived from auto-calibration signal (ACS) lines that are acquired in parallel to the reduced lines. In this paper, a cross sampling method is proposed to acquire the ACS lines orthogonal to the reduced lines. This cross sampling method increases the amount of calibration data along the direction that the k-space is undersampled and thus improves the calibration accuracy, especially when a small number of ACS lines are acquired. Both phantom and in vivo experiments demonstrate that the proposed method, named cross-sampled GRAPPA (CS-GRAPPA), can effectively reduce the aliasing artifacts of GRAPPA when high acceleration is desired.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, WI 53201, USA. haifeng@uwm.edu
authors.forename: Haifeng
authors.initials: H
authors.lastname: Wang
authors.forename: Dong
authors.initials: D
authors.lastname: Liang
authors.forename: Kevin F
authors.initials: KF
authors.lastname: King
authors.forename: Gajanan
authors.initials: G
authors.lastname: Nagarsekar
authors.forename: Leslie
authors.initials: L
authors.lastname: Ying
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity