Name
Title | ||
|---|---|---|
A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps. |
Abstract | ||
|---|---|---|
A phase map can be obtained from the real and imaginary components of a complex valued magnetic resonance (MR) image. Many applications, such as MR phase velocity mapping and susceptibility mapping, make use of the information contained in the MR phase maps. Unfortunately, noise in the complex MR signal affects the measurement of parameters related to phase (e.g, the phase velocity). In this paper, we propose a nonlocal maximum likelihood (NLML) estimation method for enhancing phase maps. The proposed method estimates the true underlying phase map from a noisy MR phase map. Experiments on both simulated and real data sets indicate that the proposed NLML method has a better performance in terms of qualitative and quantitative evaluations when compared to state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11760-016-1039-6 | Signal, Image and Video Processing |
Keywords | Field | DocType |
Denoising, Magnetic resonance image, Maximum likelihood estimation, Noise, Phase map | Noise reduction,Computer vision,Data set,Pattern recognition,Quantitative Evaluations,Phase velocity,Maximum likelihood,Artificial intelligence,Mathematics,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
11 | 5 | 1863-1703 |
Citations | PageRank | References |
1 | 0.35 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. V. Sudeep | 1 | 27 | 3.44 |
| P. Palanisamy | 2 | 75 | 10.34 |
| Chandrasekharan Kesavadas | 3 | 8 | 2.45 |
| Jan Sijbers | 4 | 634 | 69.73 |
| Arnold J den Dekker | 5 | 165 | 18.69 |
| Jeny Rajan | 6 | 113 | 18.07 |