title: Mapping mean and fluctuating velocities by Bayesian multipoint MR velocity encoding-validation against 3D particle tracking velocimetry.
abstract: OBJECTIVE
  To validate Bayesian multipoint MR velocity encoding against particle tracking velocimetry for measuring velocity vector fields and fluctuating velocities in a realistic aortic model.
  
  
  METHODS
  An elastic cast of a human aortic arch equipped with an 80 or 64% stenotic section was driven by a pulsatile pump. Peak velocities and peak turbulent kinetic energies of more than 3 m/s and 1000 J/m(3) could be generated. Velocity vector fields and fluctuating velocities were assessed using Bayesian multipoint MR velocity encoding with varying numbers of velocity encoding points and particle tracking velocimetry in the ascending aorta.
  
  
  RESULTS
  Velocities and turbulent kinetic energies measured with 5-fold k-t undersampled 10-point MR velocity encoding and particle tracking velocimetry were found to reveal good correlation with mean differences of -4.8 ± 13.3 cm/s and r(2) = 0.98 for velocities and -21.8 ± 53.9 J/m(3) and r(2) = 0.98 for turbulent kinetic energies, respectively. Three-dimensional velocity patterns of fast flow downstream of the stenoses and regions of elevated velocity fluctuations were found to agree well.
  
  
  CONCLUSIONS
  Accelerated Bayesian multipoint MR velocity encoding has been demonstrated to be accurate for assessing mean and fluctuating velocities against the reference standard particle tracking velocimetry. The MR method holds considerable potential to map velocity vector fields and turbulent kinetic energies in clinically feasible exam times of <15 min.
authors.affiliation: Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
authors.forename: Verena
authors.initials: V
authors.lastname: Knobloch
authors.forename: Christian
authors.initials: C
authors.lastname: Binter
authors.forename: Utku
authors.initials: U
authors.lastname: Gülan
authors.forename: Andreas
authors.initials: A
authors.lastname: Sigfridsson
authors.forename: Markus
authors.initials: M
authors.lastname: Holzner
authors.forename: Beat
authors.initials: B
authors.lastname: Lüthi
authors.forename: Sebastian
authors.initials: S
authors.lastname: Kozerke
mesh_terms: D000465:Algorithms; D001011:Aorta / Q000503:physiopathology*; D001024:Aortic Valve Stenosis / Q000175:diagnosis* / Q000503:physiopathology*; D001499:Bayes Theorem; D001783:Blood Flow Velocity; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012212:Rheology / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: aortic stenosis; particle tracking velocimetry; phase contrast MRI; turbulent flow