Unwrapping of MR phase images using a Markov random field model.
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.
Ying
Lei
L
Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, WI 53201, USA. leiying@uwm.edu
Liang
Zhi-Pei
ZP
Munson
David C
DC
Koetter
Ralf
R
Frey
Brendan J
BJ
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity