A new phase image reconstruction method using Markov random fields
Phase images derived from imaging devices are usually wrapped into discontinuous images, so phase unwrapping is needed for phase image reconstruction. The wrap counts of every voxel are determined by the assumption that the true phase is spatially continuous. However, it is difficult to distinguish whether the phase jump is caused by phase wrap or noise. In this paper, a new 3D phase unwrapping method is proposed by using Markov Random Fields. Phase unwrapping is formulated as a discrete energy minimization problem defined on a 3D MRF. Experimental results show substantial improvements in phase unwrapping and quantitative susceptibility mapping reconstruction compared to a region growing method on Magnetic Resonance data with low SNR and rapid phase variation.