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Sparse Bayesian pMRI Reconstruction With Complex Bernoulli-Laplace Mixture Priors

Abstract : This paper presents a sparse Bayesian regularization technique for image restoration in parallel magnetic resonance imaging (pMRI). This technique is based on a hierarchical Bayesian model that solves the inverse problem of pMRI reconstruction by promoting sparsity using a Bernoulli-Laplace mixture prior. A Markov Chain Monte Carlo (MCMC) sampling technique is used to numerically approximate the target posterior. Our model allows handling complex-valued data. Promising results obtained on synthetic data demonstrate the performance of the proposed sparse Bayesian restoration model to provide accurate estimation of the target images.
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Submitted on : Thursday, September 24, 2020 - 9:58:41 AM
Last modification on : Wednesday, June 9, 2021 - 10:00:34 AM
Long-term archiving on: : Thursday, December 3, 2020 - 4:36:39 PM


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  • HAL Id : hal-02947757, version 1
  • OATAO : 26420


Siwar Chaabene, Lotfi Chaâri, Abdelaziz Kallel. Sparse Bayesian pMRI Reconstruction With Complex Bernoulli-Laplace Mixture Priors. 4th IEEE Middle East Conference on Biomedical Engineering (MECBME 2018), Mar 2018, Tunis, Tunisia. pp.193-197. ⟨hal-02947757⟩



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