title: Autocalibrated wave-CAIPI reconstruction; Joint optimization of k-space trajectory and parallel imaging reconstruction.
abstract: Fast MRI acquisitions often rely on efficient traversal of k-space and hardware limitations, or other physical effects can cause the k-space trajectory to deviate from a theoretical path in a manner dependent on the image prescription and protocol parameters. Additional measurements or generalized calibrations are typically needed to characterize the discrepancies. We propose an autocalibrated technique to determine these discrepancies.
  
  
  
  A joint optimization is used to estimate the trajectory simultaneously with the parallel imaging reconstruction, without the need for additional measurements. Model reduction is introduced to make this optimization computationally efficient, and to ensure final image quality.
  
  
  
  We demonstrate our approach for the wave-CAIPI fast acquisition method that uses a corkscrew k-space path to efficiently encode k-space and spread the voxel aliasing. Model reduction allows for the 3D trajectory to be automatically calculated in fewer than 30 s on standard vendor hardware. The method achieves equivalent accuracy to full-gradient calibration scans.
  
  
  
  The proposed method allows for high-quality wave-CAIPI reconstruction across wide ranges of protocol parameters, such as field of view (FOV) location/orientation, bandwidth, echo time (TE), resolution, and sinusoidal amplitude/frequency. Our framework should allow for the autocalibration of gradient trajectories from many other fast MRI techniques in clinically relevant time. Magn Reson Med 78:1093-1099, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.|Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
authors: Stephen F
authors: SF
authors: Cauley
authors: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.|Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
authors: Kawin
authors: K
authors: Setsompop
authors: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.|Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
authors: Berkin
authors: B
authors: Bilgic
authors: Siemens Medical Solutions, Malvern, Pennsylvania, USA.
authors: Himanshu
authors: H
authors: Bhat
authors: Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.|Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA.
authors: Borjan
authors: B
authors: Gagoski
authors: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.|Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.|Harvard-MIT Division of Health Sciences and Technology; Institute of Medical Engineering and Science, MIT, Cambridge, Massachusetts, USA.
authors: Lawrence L
authors: LL
authors: Wald
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: autocalibrated; gradient trajectory; joint optimization; wave-CAIPI