title: Trajectory Auto-Corrected image reconstruction.
abstract: To estimate k-space trajectory errors in non-Cartesian acquisitions and reconstruct distortion-free images, without trajectory measurements or gradient calibrations.
  
  
  
  The Trajectory Auto-Corrected image Reconstruction method jointly estimates k-space trajectory errors and images, based on SENSE and SPIRiT parallel imaging reconstruction. The underlying idea is that parallel imaging and oversampling in the center of k-space provides data redundancy that can be exploited to simultaneously reconstruct images and correct trajectory errors. Trajectory errors are represented as weighted sums of trajectory-dependent error basis functions, the coefficients of which are estimated using gradient-based optimization.
  
  
  
  Trajectory Auto-Corrected image Reconstruction was applied to reconstruct images and errors in golden angle radial, center-out radial, and spiral in vivo 7 Tesla brain acquisitions in five subjects. Compared to reconstructions using nominal trajectories, Trajectory auto-corrected image reconstructions contained considerably less blurring and streaking and were of similar quality to images reconstructed using measured k-space trajectories in the center-out radial and spiral cases. Reconstruction cost function reductions and improvements in normalized image gradient squared were also similar to those for images reconstructed using measured trajectories.
  
  
  
  Trajectory Auto-Corrected image Reconstruction enables non-Cartesian image reconstructions free from trajectory errors without the need for separate gradient calibrations or trajectory measurements. Magn Reson Med 76:757-768, 2016. © 2015 Wiley Periodicals, Inc.
authors.affiliation: Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, USA.|Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
authors.forename: Julianna D
authors.initials: JD
authors.lastname: Ianni
authors.affiliation: Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, USA.|Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.|Department of Radiology, Vanderbilt University, Nashville, Tennessee, USA.|Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
authors.forename: William A
authors.initials: WA
authors.lastname: Grissom
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: center-out radial; eddy currents; golden angle radial; image reconstruction; non-Cartesian imaging; parallel imaging; radial; spiral