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Noise Modelling in Parallel Magnetic Resonance Imaging: A Variational Approach

  • Adrián MartínEmail author
  • Emanuele Schiavi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)

Abstract

We proposed a new variational model for parallel Magnetic Resonance Imaging (MRI) processing including denoising, deblurring and super-resolution. In the context of Maximum A Posteriori (MAP) estimation it takes into account the non-central \(\chi \) (nc-\(\chi \)) distribution of the noise in parallel magnitude magnetic resonance (MR) images. This leads to the resolution of an energy minimization problem. In this Bayesian modelling framework the Total Generalized Variation (TGV) is proposed as the regularization term. A primal-dual algorithm is then implemented to solve numerically the presented model. The effectiveness of our approach is shown through a successful comparison of its performance to previous TGV methods for MRI denoising based on Gaussian noise.

Keywords

Central Slice Total Generalize Variation Magnetic Resonance Image Group Fast Magnetic Resonance Image Parallel Magnetic Resonance Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Applied Mathematics DepartmentUniversidad Rey Juan CarlosMóstoles, MadridSpain
  2. 2.Magnetic Resonance Imaging Group, RLE-EECSMassachusetts Institute of TechnologyCambridgeUSA

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