title: Improved parallel MR imaging using a coefficient penalized regularization for GRAPPA reconstruction.
abstract: A novel coefficient penalized regularization method for generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction is developed for improving MR image quality. In this method, the fitting coefficients of the source data are weighted with different penalty factors, which are highly dependent upon the relative displacements from the source data to the target data in k-space. The imaging data from both phantom testing and in vivo MRI experiments demonstrate that the coefficient penalized regularization method in GRAPPA reconstruction is able to reduce noise amplification to a greater degree. Therefore, the method enhances the quality of images significantly when compared to the previous least squares and Tikhonov regularization methods.
authors.affiliation: Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Peking University, Beijing, China.
authors.forename: Wentao
authors.initials: W
authors.lastname: Liu
authors.forename: Xin
authors.initials: X
authors.lastname: Tang
authors.forename: Yajun
authors.initials: Y
authors.lastname: Ma
authors.forename: Jia-Hong
authors.initials: JH
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity