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Sci Rep. 2019 Feb 28;9(1):3034. doi: 10.1038/s41598-019-39888-7.

ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging.

Author information

1
Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany. christian.holme@med.uni-goettingen.de.
2
German Centre for Cardiovascular Research (DZHK), Partner site Göttingen, Göttingen, Germany. christian.holme@med.uni-goettingen.de.
3
Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany.
4
German Centre for Cardiovascular Research (DZHK), Partner site Göttingen, Göttingen, Germany.
5
Departement of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA.

Abstract

Robustness against data inconsistencies, imaging artifacts and acquisition speed are crucial factors limiting the possible range of applications for magnetic resonance imaging (MRI). Therefore, we report a novel calibrationless parallel imaging technique which simultaneously estimates coil profiles and image content in a relaxed forward model. Our method is robust against a wide class of data inconsistencies, minimizes imaging artifacts and is comparably fast, combining important advantages of many conceptually different state-of-the-art parallel imaging approaches. Depending on the experimental setting, data can be undersampled well below the Nyquist limit. Here, even high acceleration factors yield excellent imaging results while being robust to noise and the occurrence of phase singularities in the image domain, as we show on different data. Moreover, our method successfully reconstructs acquisitions with insufficient field-of-view. We further compare our approach to ESPIRiT and SAKE using spin-echo and gradient echo MRI data from the human head and knee. In addition, we show its applicability to non-Cartesian imaging on radial FLASH cardiac MRI data. Using theoretical considerations, we show that ENLIVE can be related to a low-rank formulation of blind multi-channel deconvolution, explaining why it inherently promotes low-rank solutions.

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