title: Resolving phase ambiguity in dual-echo dixon imaging using a projected power method.
abstract: To develop a fast and robust method to resolve phase ambiguity in dual-echo Dixon imaging.
  
  
  
  A major challenge in dual-echo Dixon imaging is to estimate the phase error resulting from field inhomogeneity. In this work, a binary quadratic optimization program was formulated to resolve the phase ambiguity. A projected power method was developed to efficiently solve the optimization problem. Both the 1-peak fat model and 6-peak fat model were applied to three-dimensional (3D) datasets. Additionally, the proposed method was extended to dynamic magnetic resonance imaging (MRI) applications using the 6-peak fat model. With institutional review board (IRB) approval and patient consent/assent, the proposed method was evaluated and compared with region growing on 29 consecutive 3D high-resolution patient datasets.
  
  
  
  Fast and robust water/fat separation was achieved by the proposed method in different representative 3D datasets and dynamic 3D datasets. Superior water/fat separation was achieved using the 6-peak fat model compared with the 1-peak fat model. Compared to region growing, the proposed method reduced water/fat swaps from 76 to 7% of the patient cohort.
  
  
  
  The proposed method can achieve fast and robust phase error estimation in dual-echo Dixon imaging. Magn Reson Med 77:2066-2076, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Department of Radiology, Stanford University, Stanford, California, USA.|Department of Electrical Engineering, Stanford University, Stanford, California, USA.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Department of Electrical Engineering, Princeton University, Princeton, New Jersey, USA.
authors.forename: Yuxin
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Radiology, Stanford University, Stanford, California, USA.
authors.forename: Shanshan
authors.initials: S
authors.lastname: Bao
authors.affiliation: Department of Radiology, Stanford University, Stanford, California, USA.
authors.forename: Marcus T
authors.initials: MT
authors.lastname: Alley
authors.affiliation: Department of Electrical Engineering, Stanford University, Stanford, California, USA.
authors.forename: John M
authors.initials: JM
authors.lastname: Pauly
authors.affiliation: Department of Radiology, Stanford University, Stanford, California, USA.|Department of Electrical Engineering, Stanford University, Stanford, California, USA.
authors.forename: Brian A
authors.initials: BA
authors.lastname: Hargreaves
authors.affiliation: Department of Radiology, Stanford University, Stanford, California, USA.
authors.forename: Shreyas S
authors.initials: SS
authors.lastname: Vasanawala
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016477:Artifacts; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007223:Infant; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical
keywords: binary quadratic optimization; dynamic Dixon; fat suppression; two-point Dixon; water-fat separation