Interferometric image reconstruction as a nonlinear Bayesian estimation problem
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Instantaneous frequency estimation using stochastic calculus and bootstrapping
EURASIP Journal on Applied Signal Processing
Phase sensitive reconstruction for water/fat separation in MR imaging using inverse gradient
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
A factorized variational technique for phase unwrapping in Markov random fields
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.01 |
The authors have developed a technique based on a solution of the Poisson equation to unwrap the phase in magnetic resonance (MR) phase images. The method is based on the assumption that the magnitude of the inter-pixel phase change is less than π per pixel. Therefore, the authors obtain an estimate of the phase gradient by “wrapping” the gradient of the original phase image. The problem is then to obtain the absolute phase given the estimate of the phase gradient. The least-squares (LS) solution to this problem is shown to be a solution of the Poisson equation allowing the use of fast Poisson solvers. The absolute phase is then obtained by mapping the LS phase to the nearest multiple of 2 K from the measured phase. The proposed technique is evaluated using MR phase images and is proven to be robust in the presence of noise. An application of the proposed method to the 3-point Dixon technique for water and fat separation is demonstrated