Unwrapping of MR phase images using a Markov random field model.

  • Ying L
  • Liang Z
  • Munson 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
  • biological
  • brain
  • brain anatomy & histology
  • computer assisted
  • computer assisted methods
  • computer simulation
  • humans
  • image enhancement
  • image enhancement methods
  • image interpretation
  • imaging
  • information storage retrieval
  • information storage retrieval methods
  • magnetic resonance imaging
  • magnetic resonance imaging instrumentation
  • magnetic resonance imaging methods
  • markov chains
  • models
  • phantoms
  • reproducibility results
  • sensitivity specificity
  • statistical
  • three dimensional
  • three dimensional methods

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  • Lei Ying Lei Ying

  • Zhi-Pei Liang Zhi-Pei Liang

  • D C Jr Munson

  • R Koetter

  • B J Frey

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