title: Rosette spectroscopic imaging: optimal parameters for alias-free, high sensitivity spectroscopic imaging.
abstract: OBJECTIVE
  To optimize the Rosette trajectories for fast, high sensitivity spectroscopic imaging experiments and to compare this acquisition technique with other chemical shift imaging (CSI) methods.
  
  
  METHODS
  A framework for comparing the sensitivity of the Rosette Spectroscopic Imaging (RSI) acquisition to other spectroscopic imaging experiments is outlined. Accounting for hardware constraints, trajectory parameters that provide for optimal sampling and minimal artifact production are found. Along with an analytical expression for the number of excitations to be used in an RSI experiment that is provided, the theoretical precompensation weights used for optimal image reconstruction are derived.
  
  
  RESULTS
  The spectral response function for RSI is shown to be approximately the same as the point spread function of standard Fourier reconstructions. While the signal-to-noise ratio (SNR) for an RSI experiment is reduced by the inherent nonuniform sampling of these trajectories, their circular k-space support and speed of spatial encoding leads to greater SNR efficiency and improvements in the total data acquisition time relative to the gold standard CSI approach with square k-space support and to similar efficiency to spiral CSI acquisitions. Numerical simulations and in vivo experimental data are presented to demonstrate the properties of this data acquisition technique.
  
  
  CONCLUSIONS
  This work demonstrates the use of Rosette trajectories and how to achieve improved efficiency for these trajectories in a two-dimensional spectroscopic imaging experiment.
authors.affiliation: University of Pittsburgh, Department of Physics, Pittsburgh, Pennsylvania, USA. claudiu.schirda@gmail.com
authors.forename: Claudiu V
authors.initials: CV
authors.lastname: Schirda
authors.forename: Costin
authors.initials: C
authors.lastname: Tanase
authors.forename: Fernando E
authors.initials: FE
authors.lastname: Boada
mesh_terms: D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007866:Leg / Q000033:anatomy & histology; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted