title: A novel phase-unwrapping method based on pixel clustering and local surface fitting with application to Dixon water-fat MRI.
abstract: OBJECTIVE
  To develop and evaluate a novel 2D phase-unwrapping method that works robustly in the presence of severe noise, rapid phase changes, and disconnected regions.
  
  
  METHODS
  The MR phase map usually varies rapidly in regions adjacent to wraps. In contrast, the phasors can vary slowly, especially in regions distant from tissue boundaries. Based on this observation, this paper develops a phase-unwrapping method by using a pixel clustering and local surface fitting (CLOSE) approach to exploit different local variation characteristics between the phase and phasor data. The CLOSE approach classifies pixels into easy-to-unwrap blocks and difficult-to-unwrap residual pixels first, and then sequentially performs intrablock, interblock, and residual-pixel phase unwrapping by a region-growing surface-fitting method. The CLOSE method was evaluated on simulation and in vivo water-fat Dixon data, and was compared with phase region expanding labeler for unwrapping discrete estimates (PRELUDE).
  
  
  RESULTS
  In the simulation experiment, the mean error ratio by CLOSE was less than 1.50%, even in areas with signal-to-noise ratio equal to 0.5, phase changes larger than π, and disconnected regions. For 350 in vivo knee and ankle images, the water-fat swap ratio of CLOSE was 4.29%, whereas that of PRELUDE was 25.71%.
  
  
  CONCLUSIONS
  The CLOSE approach can correctly unwrap phase with high robustness, and benefit MRI applications that require phase unwrapping. Magn Reson Med 79:515-528, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.|Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Junying
authors.initials: J
authors.lastname: Cheng
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Yingjie
authors.initials: Y
authors.lastname: Mei
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Biaoshui
authors.initials: B
authors.lastname: Liu
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Jijing
authors.initials: J
authors.lastname: Guan
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Liu
authors.affiliation: Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, China.|Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong SAR, China.
authors.forename: Ed X
authors.initials: EX
authors.lastname: Wu
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.|Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.affiliation: Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Yanqiu
authors.initials: Y
authors.lastname: Feng
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D000465:Algorithms; D000842:Ankle / Q000000981:diagnostic imaging; D001921:Brain / Q000000981:diagnostic imaging; D016000:Cluster Analysis; D003198:Computer Simulation; D064368:Healthy Volunteers; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D007717:Knee / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D016011:Normal Distribution; D059629:Signal-To-Noise Ratio; D014867:Water
chemical_list: D014867:Water
keywords: local polynomial surface fitting; phase unwrapping; pixel clustering; thresholding; water-fat separation