title: O-space with high resolution readouts outperforms radial imaging.
abstract: While O-Space imaging is well known to accelerate image acquisition beyond traditional Cartesian sampling, its advantages compared to undersampled radial imaging, the linear trajectory most akin to O-Space imaging, have not been detailed. In addition, previous studies have focused on ultrafast imaging with very high acceleration factors and relatively low resolution. The purpose of this work is to directly compare O-Space and radial imaging in their potential to deliver highly undersampled images of high resolution and minimal artifacts, as needed for diagnostic applications. We report that the greatest advantages to O-Space imaging are observed with extended data acquisition readouts.
  
  
  
  A sampling strategy that uses high resolution readouts is presented and applied to compare the potential of radial and O-Space sequences to generate high resolution images at high undersampling factors. Simulations and phantom studies were performed to investigate whether use of extended readout windows in O-Space imaging would increase k-space sampling and improve image quality, compared to radial imaging.
  
  
  
  Experimental O-Space images acquired with high resolution readouts show fewer artifacts and greater sharpness than radial imaging with equivalent scan parameters. Radial images taken with longer readouts show stronger undersampling artifacts, which can cause small or subtle image features to disappear. These features are preserved in a comparable O-Space image.
  
  
  
  High resolution O-Space imaging yields highly undersampled images of high resolution and minimal artifacts. The additional nonlinear gradient field improves image quality beyond conventional radial imaging.
authors.affiliation: Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA.
authors.forename: Haifeng
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA.
authors.forename: Leo
authors.initials: L
authors.lastname: Tam
authors.affiliation: Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA.
authors.forename: Emre
authors.initials: E
authors.lastname: Kopanoglu
authors.affiliation: Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA.
authors.forename: Dana C
authors.initials: DC
authors.lastname: Peters
authors.affiliation: Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA; Department of Neurosurgery, Yale University, New Haven, CT 06520, USA.
authors.forename: R Todd
authors.initials: RT
authors.lastname: Constable
authors.affiliation: Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA. Electronic address: gigi.galiana@yale.edu.
authors.forename: Gigi
authors.initials: G
authors.lastname: Galiana
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results
keywords: High-resolution; Nonlinear spatial encoding; O-space imaging; Parallel imaging