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• W1758598986 abstract "Purpose To propose and evaluate P‐LORAKS a new calibrationless parallel imaging reconstruction framework. Theory and Methods LORAKS is a flexible and powerful framework that was recently proposed for constrained MRI reconstruction. LORAKS was based on the observation that certain matrices constructed from fully sampled k‐space data should have low rank whenever the image has limited support or smooth phase, and made it possible to accurately reconstruct images from undersampled or noisy data using low‐rank regularization. This paper introduces P‐LORAKS, which extends LORAKS to the context of parallel imaging. This is achieved by combining the LORAKS matrices from different channels to yield a larger but more parsimonious low‐rank matrix model of parallel imaging data. This new model can be used to regularize the reconstruction of undersampled parallel imaging data, and implicitly imposes phase, support, and parallel imaging constraints without needing to calibrate phase, support, or sensitivity profiles. Results The capabilities of P‐LORAKS are evaluated with retrospectively undersampled data and compared against existing parallel MRI reconstruction methods. Results show that P‐LORAKS can improve parallel imaging reconstruction quality, and can enable the use of new k‐space trajectories that are not compatible with existing reconstruction methods. Conclusion The P‐LORAKS framewok provides a new and effective way to regularize parallel imaging reconstruction. Magn Reson Med 75:1499–1514, 2016. © 2015 Wiley Periodicals, Inc." @default.
• W1758598986 created "2016-06-24" @default.
• W1758598986 creator A5031370363 @default.
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• W1758598986 date "2015-05-07" @default.
• W1758598986 modified "2023-10-11" @default.
• W1758598986 title "P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data" @default.
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• W1758598986 doi "https://doi.org/10.1002/mrm.25717" @default.
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