title: Image reconstruction using a gradient impulse response model for trajectory prediction.
abstract: Gradient imperfections remain a challenge in MRI, especially for sequences relying on long imaging readouts. This work aims to explore image reconstruction based on k-space trajectories predicted by an impulse response model of the gradient system.
  
  
  
  Gradient characterization was performed twice with 3 years interval on a commercial 3 Tesla (T) system. The measured gradient impulse response functions were used to predict actual k-space trajectories for single-shot echo-planar imaging (EPI), spiral and variable-speed EPI sequences. Image reconstruction based on the predicted trajectories was performed for phantom and in vivo data. Resulting images were compared with reconstructions based on concurrent field monitoring, separate trajectory measurements, and nominal trajectories.
  
  
  
  Image reconstruction using model-based trajectories yielded high-quality images, comparable to using separate trajectory measurements. Compared with using nominal trajectories, it strongly reduced ghosting, blurring, and geometric distortion. Equivalent image quality was obtained with the recent characterization and that performed 3 years prior.
  
  
  
  Model-based trajectory prediction enables high-quality image reconstruction for technically challenging sequences such as single-shot EPI and spiral imaging. It thus holds great promise for fast structural imaging and advanced neuroimaging techniques, including functional MRI, diffusion tensor imaging, and arterial spin labeling. The method can be based on a one-time system characterization as demonstrated by successful use of 3-year-old calibration data. Magn Reson Med 76:45-58, 2016. © 2015 Wiley Periodicals, Inc.
authors.affiliation: Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
authors.forename: S Johanna
authors.initials: SJ
authors.lastname: Vannesjo
authors.affiliation: Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
authors.forename: Nadine N
authors.initials: NN
authors.lastname: Graedel
authors.affiliation: Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.|Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
authors.forename: Lars
authors.initials: L
authors.lastname: Kasper
authors.affiliation: Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
authors.forename: Simon
authors.initials: S
authors.lastname: Gross
authors.affiliation: Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
authors.forename: Julia
authors.initials: J
authors.lastname: Busch
authors.affiliation: Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
authors.forename: Maximilian
authors.initials: M
authors.lastname: Haeberlin
authors.affiliation: Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.|Skope Magnetic Resonance Technologies, Zurich, Switzerland.
authors.forename: Christoph
authors.initials: C
authors.lastname: Barmet
authors.affiliation: Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
authors.forename: Klaas P
authors.initials: KP
authors.lastname: Pruessmann
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: EPI; GIRF; linear time-invariant (LTI); magnetic field monitoring; single-shot imaging; spiral imaging