title: Phase unwrapping with graph cuts optimization and dual decomposition acceleration for 3D high-resolution MRI data.
abstract: Existence of low SNR regions and rapid-phase variations pose challenges to spatial phase unwrapping algorithms. Global optimization-based phase unwrapping methods are widely used, but are significantly slower than greedy methods. In this paper, dual decomposition acceleration is introduced to speed up a three-dimensional graph cut-based phase unwrapping algorithm.
  
  
  
  The phase unwrapping problem is formulated as a global discrete energy minimization problem, whereas the technique of dual decomposition is used to increase the computational efficiency by splitting the full problem into overlapping subproblems and enforcing the congruence of overlapping variables. Using three dimensional (3D) multiecho gradient echo images from an agarose phantom and five brain hemorrhage patients, we compared this proposed method with an unaccelerated graph cut-based method.
  
  
  
  Experimental results show up to 18-fold acceleration in computation time.
  
  
  
  Dual decomposition significantly improves the computational efficiency of 3D graph cut-based phase unwrapping algorithms. Magn Reson Med 77:1353-1358, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Department of Automation, Tsinghua University, Beijing, China.|National Computer Network Emergency Response Technical Team/Coordination Center (CNCERT/CC), Beijing, China.
authors.forename: Jianwu
authors.initials: J
authors.lastname: Dong
authors.affiliation: Department of Automation, Tsinghua University, Beijing, China.|Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, China.|Beijing Key Laboratory of Security in Big Data Processing and Application.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
authors.affiliation: Department of Radiology, Weill Cornell Medical College, New York, New York, USA.
authors.forename: Dong
authors.initials: D
authors.lastname: Zhou
authors.affiliation: Medimagemetric, LLC, New York, New York, USA.
authors.forename: Tian
authors.initials: T
authors.lastname: Liu
authors.affiliation: Department of Automation, Tsinghua University, Beijing, China.
authors.forename: Zhaofei
authors.initials: Z
authors.lastname: Yu
authors.affiliation: Department of Radiology, Weill Cornell Medical College, New York, New York, USA.|Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA.|Department of Biomedical Engineering, Kyung Hee University, Korea.
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D002543:Cerebral Hemorrhage / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Markov random field; dual decomposition; graph cuts; phase unwrapping