Unwrapping of MR phase images using a Markov random field model

  • Ying L
  • Liang Z
  • Munson Jr. D
 et al. 
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Phase unwrapping is an important problem in many magnetic resonance imaging applications, such as field mapping and flow imaging. The challenge in two-dimensional phase unwrapping lies in distinguishing jumps due to phase wrapping from those due to noise and/or abrupt variations in the actual function. This paper addresses this problem using a Markov random field to model the true phase function, whose parameters are determined by maximizing the a posteriori probability. To reduce the computational complexity of the optimization procedure, an efficient algorithm is also proposed for parameter estimation using a series of dynamic programming connected by the iterated conditional modes. The proposed method has been tested with both simulated and experimental data, yielding better results than some of the state-of-the-art method (e.g., the popular least-squares method) in handling noisy phase images with rapid phase variations.

Author-supplied keywords

  • *Algorithms
  • Brain/*anatomy & histology
  • Computer Simulation
  • Humans
  • Image Enhancement/*methods
  • Image Interpretation, Computer-Assisted/*methods
  • Imaging, Three-Dimensional/*methods
  • Information Storage and Retrieval/methods
  • Magnetic Resonance Imaging/instrumentation/*method
  • Markov Chains
  • Models, Biological
  • Models, Statistical
  • Phantoms, Imaging
  • Reproducibility of Results
  • Sensitivity and Specificity

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  • L Ying

  • Z P Liang

  • D C Munson Jr.

  • R Koetter

  • B J Frey

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