title: Pseudo-random center placement O-space imaging for improved incoherence compressed sensing parallel MRI.
abstract: OBJECTIVE
  Nonlinear spatial encoding magnetic (SEM) field strategies such as O-space imaging have previously reported dispersed artifacts during accelerated scans. Compressed sensing (CS) has shown a sparsity-promoting convex program allows image reconstruction from a reduced data set when using the appropriate sampling. The development of a pseudo-random center placement (CP) O-space CS approach optimizes incoherence through SEM field modulation to reconstruct an image with reduced error.
  
  
  METHODS
  The incoherence parameter determines the sparsity levels for which CS is valid and the related transform point spread function measures the maximum interference for a single point. The O-space acquisition is optimized for CS by perturbing the Z(2) strength within 30% of the nominal value and demonstrated on a human 3T scanner.
  
  
  RESULTS
  Pseudo-random CP O-space imaging is shown to improve incoherence between the sensing and sparse domains. Images indicate pseudo-random CP O-space has reduced mean squared error compared with a typical linear SEM field acquisition method.
  
  
  CONCLUSIONS
  Pseudo-random CP O-space imaging, with a nonlinear SEM field designed for CS, is shown to reduce mean squared error of images at high acceleration over linear encoding methods for a 2D slice when using an eight channel circumferential receiver array for parallel imaging.
authors.affiliation: Yale University, Department of Diagnostic Radiology, New Haven, Connecticut, USA.
authors.forename: Leo K
authors.initials: LK
authors.lastname: Tam
authors.affiliation: Yale University, Department of Diagnostic Radiology, New Haven, Connecticut, USA.
authors.forename: Gigi
authors.initials: G
authors.lastname: Galiana
authors.affiliation: Massachusetts General Hospital Martinos Center for Imaging, Boston, Massachusetts, USA.
authors.forename: Jason P
authors.initials: JP
authors.lastname: Stockmann
authors.affiliation: Yale University, Department of Diagnostic Radiology, New Haven, Connecticut, USA.|Yale University, Department of Electrical Engineering, New Haven, Connecticut, USA.|Yale University, Department of Biomedical Engineering, New Haven, Connecticut, USA.
authors.forename: Hemant
authors.initials: H
authors.lastname: Tagare
authors.affiliation: Yale University, Department of Diagnostic Radiology, New Haven, Connecticut, USA.
authors.forename: Dana C
authors.initials: DC
authors.lastname: Peters
authors.affiliation: Yale University, Department of Diagnostic Radiology, New Haven, Connecticut, USA.|Yale University, Department of Biomedical Engineering, New Haven, Connecticut, USA.|Yale University, Department of Neurosurgery, New Haven, Connecticut, USA.
authors.forename: R Todd
authors.initials: RT
authors.lastname: Constable
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*
keywords: O-space; accelerated imaging; compressed sensing; incoherence; nonlinear gradient encoding; parallel imaging; sparsity; spatial encoding magnetic fields; transform point spread function