title: Non-cartesian MRI reconstruction with automatic regularization Via Monte-Carlo SURE.
abstract: Magnetic resonance image (MRI) reconstruction from undersampled k-space data requires regularization to reduce noise and aliasing artifacts. Proper application of regularization however requires appropriate selection of associated regularization parameters. In this work, we develop a data-driven regularization parameter adjustment scheme that minimizes an estimate [based on the principle of Stein's unbiased risk estimate (SURE)] of a suitable weighted squared-error measure in k-space. To compute this SURE-type estimate, we propose a Monte-Carlo scheme that extends our previous approach to inverse problems (e.g., MRI reconstruction) involving complex-valued images. Our approach depends only on the output of a given reconstruction algorithm and does not require knowledge of its internal workings, so it is capable of tackling a wide variety of reconstruction algorithms and nonquadratic regularizers including total variation and those based on the l1-norm. Experiments with simulated and real MR data indicate that the proposed approach is capable of providing near mean squared-error optimal regularization parameters for single-coil undersampled non-Cartesian MRI reconstruction.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA. sramani@umich.edu
authors.forename: Sathish
authors.initials: S
authors.lastname: Ramani
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Weller
authors.forename: Jon-Fredrik
authors.initials: JF
authors.lastname: Nielsen
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Fessler
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009010:Monte Carlo Method; D019047:Phantoms, Imaging