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Parallel imaging with nonlinear reconstruction using variational penalties
Author(s) -
Knoll Florian,
Clason Christian,
Bredies Kristian,
Uecker Martin,
Stollberger Rudolf
Publication year - 2012
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.22964
Subject(s) - imaging phantom , nonlinear system , regularization (linguistics) , algorithm , sampling (signal processing) , total variation denoising , iterative reconstruction , pseudorandom number generator , image quality , computer science , undersampling , mathematics , mathematical optimization , artificial intelligence , image (mathematics) , computer vision , physics , optics , filter (signal processing) , quantum mechanics
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. Magn Reson Med 67:34–41, 2012. © 2011 Wiley Periodicals, Inc.