title: A Bayesian model for highly accelerated phase-contrast MRI.
abstract: Phase-contrast magnetic resonance imaging is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial and temporal resolutions, real-time application, and extensions to four-dimensional flow imaging in clinical settings. We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique to phase-contrast magnetic resonance imaging.
  
  
  
  The proposed approach models physical correlations across space, time, and velocity encodings. The proposed Bayesian approach exploits the relationships in both magnitude and phase among velocity encodings. A fast iterative recovery algorithm is introduced based on message passing. For validation, prospectively undersampled data are processed from a pulsatile flow phantom and five healthy volunteers.
  
  
  
  The proposed approach is in good agreement, quantified by peak velocity and stroke volume (SV), with reference data for acceleration rates R≤10. For SV, Pearson r≥0.99 for phantom imaging (n = 24) and r≥0.96 for prospectively accelerated in vivo imaging (n = 10) for R≤10.
  
  
  
  The proposed approach enables accurate quantification of blood flow from highly undersampled data. The technique is extensible to four-dimensional flow imaging, where higher acceleration may be possible due to additional redundancy. Magn Reson Med 76:689-701, 2016. © 2015 Wiley Periodicals, Inc.
authors.affiliation: Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Rich
authors.affiliation: Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA.|Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA.
authors.forename: Lee C
authors.initials: LC
authors.lastname: Potter
authors.affiliation: Siemens Medical Solutions, Columbus, Ohio, USA.
authors.forename: Ning
authors.initials: N
authors.lastname: Jin
authors.affiliation: Department of Electrical Engineering, Wright State University, Dayton, Ohio, USA.
authors.forename: Joshua
authors.initials: J
authors.lastname: Ash
authors.affiliation: Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA.|Division of Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, USA.|Department of Radiology, The Ohio State University, Columbus, Ohio, USA.
authors.forename: Orlando P
authors.initials: OP
authors.lastname: Simonetti
authors.affiliation: Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA.|Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA.
authors.forename: Rizwan
authors.initials: R
authors.lastname: Ahmad
mesh_terms: D000465:Algorithms; D001011:Aorta / Q000000981:diagnostic imaging* / Q000502:physiology*; D001499:Bayes Theorem; D001783:Blood Flow Velocity / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000295:instrumentation / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Bayesian inference; approximate message passing; cardiac MRI; factor graph; flow imaging; minimum mean squared error estimation; peak blood flow; velocity