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Magn Reson Med. 2016 Apr;75(4):1499-514. doi: 10.1002/mrm.25717. Epub 2015 May 7.

P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data.

Author information

1
Department of Electrical Engineering, University of Southern California, Los Angeles, California, USA.
2
Department of Electronic Engineering, Tsinghua University, Beijing, China.

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.

KEYWORDS:

low-rank matrix recovery; parallel MRI; phase constraints; support constraints

PMID:
25952136
PMCID:
PMC4637005
DOI:
10.1002/mrm.25717
[Indexed for MEDLINE]
Free PMC Article

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