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

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.

arXiv comment17 pages, 10 figures

References in corpus (3)

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• An overview of low-rank matrix recovery from incomplete observations
• Estimating Absolute-Phase Maps Using ESPIRiT and Virtual Conjugate Coils

Cited by in corpus (6)

• Deep, Deep Learning with BART
• High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRI
• Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for Undersampled MRI Reconstruction
• Enhancing quality and speed in database-free neural network reconstructions of undersampled MRI with SCAMPI
• Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion
• MORSE: Multiple Orthogonal Reference Sensitivity Encoding