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Parallel imaging with nonlinear reconstruction using variational penalties
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DOI：10.1002/mrm.22964.png)
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Abstract
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A new approach based on nonlinear inversion for autocalibrated parallel imaging with arbitrary sampling patterns is presented. By extending the iteratively regularized GaussNewton 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 67:3441, 2012. (C) 2011 Wiley Periodicals, Inc.
Keywords:
parallel imaging
radial sampling
pseudorandom sampling
nonlinear inversion
total variation
total generalized variation
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