Signal, Image and Video Processing

, Volume 11, Issue 5, pp 913–920 | Cite as

A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps

  • P. V. Sudeep
  • P. Palanisamy
  • Chandrasekharan Kesavadas
  • Jan Sijbers
  • Arnold J. den Dekker
  • Jeny Rajan
Original Paper
  • 173 Downloads

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.

Keywords

Denoising Magnetic resonance image Maximum likelihood estimation Noise Phase map 

Notes

Acknowledgements

This work was partially supported by the Research Foundation-Flanders (FWO, Belgium) through project funding G037813N and the TOP BOF project University of Antwerp (TOP BOF project 26824).

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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • P. V. Sudeep
    • 1
    • 2
  • P. Palanisamy
    • 1
  • Chandrasekharan Kesavadas
    • 3
  • Jan Sijbers
    • 4
  • Arnold J. den Dekker
    • 4
    • 5
  • Jeny Rajan
    • 6
  1. 1.Department of Electronics and Communication EngineeringNational Institute of Technology - TiruchirappalliTiruchirappalliIndia
  2. 2.Department of Electronics and Communication EngineeringNational Institute of Technology KarnatakaSurathkalIndia
  3. 3.Department of Imaging Sciences and Intervention RadiologySree Chitra Tirunal Institute for Medical Sciences and TechnologyTrivandrumIndia
  4. 4.iMinds Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
  5. 5.Delft Center for Systems and ControlDelft University of TechnologyDelftThe Netherlands
  6. 6.Department of Computer Science and EngineeringNational Institute of Technology KarnatakaSurathkalIndia

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