title: Balanced SSFP-like steady-state imaging using small-tip fast recovery with a spectral prewinding pulse.
abstract: OBJECTIVE
  Small-tip fast recovery (STFR) imaging has been proposed recently as a potential alternative to balanced steady-state free precession (bSSFP). STFR relies on a tailored "tip-up" radio-frequency pulse to achieve comparable signal level as bSSFP, but with reduced banding artifacts and transient oscillations, and is compatible with magnetization-preparation pulses. Previous STFR implementations used two-dimensional or three-dimensional pulses spatially tailored to the accumulated phase calculated from a B0 field map, making the steady-state STFR signal contain some T2* weighting. Here, we propose to replace the spatially tailored pulse with a recently introduced spectrally selective "pre-winding" pulse that is precomputed to a target frequency range. The proposed "spectral-STFR" sequence produces T2/T1-weighted images similar to bSSFP, but with reduced banding and potentially other benefits.
  
  
  METHODS
  We investigated the steady-state signal properties of spectral-STFR using simulations, and phantom and human volunteer experiments.
  
  
  RESULTS
  Our simulation and experimental results showed that the spectral-STFR sequence has similar signal level and tissue contrast as bSSFP, but has a wider passband and more consistent banding profiles across different tissues (e.g., less hyperintense signal at band edges for low flip angles). Care is needed in designing the spectral radio-frequency pulse to ensure that the small tip angle approximation holds during radio-frequency transmission.
  
  
  CONCLUSIONS
  Spectral-STFR has similar tissue contrast as bSSFP but a wider passband and more consistent cerebrospinal fluid/brain tissue contrast across the passband. The spectral-STFR sequence is a potential alternative to bSSFP in some applications. Compared to a spatially tailored STFR sequence, spectral-STFR can be precomputed, is easier to implement in practice, and potentially has more uniform image contrast and minimal T2* weighting.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Sun
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA.|Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Fessler
authors.affiliation: Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Douglas C
authors.initials: DC
authors.lastname: Noll
authors.affiliation: Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Jon-Fredrik
authors.initials: JF
authors.lastname: Nielsen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D064368:Healthy Volunteers; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D019047:Phantoms, Imaging
keywords: STFR; bSSFP; banding artifact; steady-state imaging
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