title: P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data.
abstract: OBJECTIVE
  To propose and evaluate P-LORAKS a new calibrationless parallel imaging reconstruction framework.
  
  
  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.
  
  
  CONCLUSIONS
  The P-LORAKS framewok provides a new and effective way to regularize parallel imaging reconstruction.
authors.affiliation: Department of Electrical Engineering, University of Southern California, Los Angeles, California, USA.
authors.forename: Justin P
authors.initials: JP
authors.lastname: Haldar
authors.affiliation: Department of Electronic Engineering, Tsinghua University, Beijing, China.
authors.forename: Jingwei
authors.initials: J
authors.lastname: Zhuo
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*
keywords: low-rank matrix recovery; parallel MRI; phase constraints; support constraints