title: Reducing sensitivity losses due to respiration and motion in accelerated echo planar imaging by reordering the autocalibration data acquisition.
abstract: OBJECTIVE
  To reduce the sensitivity of echo-planar imaging (EPI) auto-calibration signal (ACS) data to patient respiration and motion to improve the image quality and temporal signal-to-noise ratio (tSNR) of accelerated EPI time-series data.
  
  
  METHODS
  ACS data for accelerated EPI are generally acquired using segmented, multishot EPI to distortion-match the ACS and time-series data. The ACS data are, therefore, typically collected over multiple TR periods, leading to increased vulnerability to motion and dynamic B0 changes. The fast low-angle excitation echo-planar technique (FLEET) is adopted to reorder the ACS segments so that segments within any given slice are acquired consecutively in time, thereby acquiring ACS data for each slice as rapidly as possible.
  
  
  RESULTS
  Subject breathhold and motion phantom experiments demonstrate that artifacts in the ACS data reduce tSNR and produce tSNR discontinuities across slices in the accelerated EPI time-series data. Accelerated EPI data reconstructed using FLEET-ACS exhibit improved tSNR and increased tSNR continuity across slices. Additionally, image quality is improved dramatically when bulk motion occurs during the ACS acquisition.
  
  
  CONCLUSIONS
  FLEET-ACS provides reduced respiration and motion sensitivity in accelerated EPI, which yields higher tSNR and image quality. Benefits are demonstrated in both conventional-resolution 3T and high-resolution 7T EPI time-series data.
authors: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.
authors: Jonathan R
authors: JR
authors: Polimeni
authors: Siemens Medical Solutions USA Inc., Charlestown, MA, USA.
authors: Himanshu
authors: H
authors: Bhat
authors: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.
authors: Thomas
authors: T
authors: Witzel
authors: Siemens AG, Healthcare Sector, Erlangen, Bavaria, Germany.
authors: Thomas
authors: T
authors: Benner
authors: Siemens AG, Healthcare Sector, Erlangen, Bavaria, Germany.
authors: Thorsten
authors: T
authors: Feiweier
authors: National Institute of Mental Health, Bethesda, MD, USA.
authors: Souheil J
authors: SJ
authors: Inati
authors: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.
authors: Ville
authors: V
authors: Renvall
authors: Siemens Medical Solutions USA Inc., Charlestown, MA, USA.
authors: Keith
authors: K
authors: Heberlein
authors: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.|Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors: Lawrence L
authors: LL
authors: Wald
mesh_terms: D000328:Adult; D001921:Brain / Q000033:anatomy & histology*; D002138:Calibration; D017352:Echo-Planar Imaging / Q000379:methods*; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D009038:Motion; D019047:Phantoms, Imaging; D012119:Respiration; D059629:Signal-To-Noise Ratio
keywords: GRAPPA; high-field fMRI; high-resolution fMRI; image reconstruction; parallel imaging