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. © 2006, IEE.
CITATION STYLE
Ying, L., Liang, Z. P., Munson, D. C., Koetter, R., & Frey, B. J. (2006). Unwrapping of MR phase images using a Markov random field model. IEEE Transactions on Medical Imaging, 25(1), 128–136. https://doi.org/10.1109/TMI.2005.861021
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