title: Parallel imaging with nonlinear reconstruction using variational penalties.
abstract: A new approach based on nonlinear inversion for autocalibrated parallel imaging with arbitrary sampling patterns is presented. By extending the iteratively regularized Gauss-Newton method with variational penalties, the improved reconstruction quality obtained from joint estimation of image and coil sensitivities is combined with the superior noise suppression of total variation and total generalized variation regularization. In addition, the proposed approach can lead to enhanced removal of sampling artifacts arising from pseudorandom and radial sampling patterns. This is demonstrated for phantom and in vivo measurements.
authors.affiliation: Institute of Medical Engineering Graz University of Technology, Graz, Austria. florian.knoll@tugraz.at
authors.forename: Florian
authors.initials: F
authors.lastname: Knoll
authors.forename: Christian
authors.initials: C
authors.lastname: Clason
authors.forename: Kristian
authors.initials: K
authors.lastname: Bredies
authors.forename: Martin
authors.initials: M
authors.lastname: Uecker
authors.forename: Rudolf
authors.initials: R
authors.lastname: Stollberger
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 / Q000379:methods*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity