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Bayesian sparse regularization for parallel MRI reconstruction using Complex Bernoulli-Laplace mixture priors

Chaabene, Siwar and Chaari, Lotfi and Kallel, Abdelaziz Bayesian sparse regularization for parallel MRI reconstruction using Complex Bernoulli-Laplace mixture priors. (2019) Signal, Image and Video Processing, 14. 445-453. ISSN 1863-1703

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Official URL: https://doi.org/10.1007/s11760-019-01567-5


Parallel imaging technique using several receiver coils provides a fast acquisition of magnetic resonance imaging (MRI) images with high temporal and/or spatial resolutions. Against this background, the most difficult task is the full field of view images reconstruction without noise, distortions and artifacts. In this context, SENSitivity Encoding is considered the most often used parallel MRI (pMRI) reconstruction method in the clinical application. On the one side, solving the inherent reconstruction problems has known significant progress during the last decade. On the other side, the sparse Bayesian regularization for signal/image recovery has generated a great research interest especially when large volumes of data are processed. The purpose of this paper is to develop a novel Bayesian regularization technique for sparse pMRI reconstruction. The new technique is based on a hierarchical Bayesian model using a complex Bernoulli–Laplace mixture in order to promote two sparsity levels for the target image. The inference is conducted using a Markov chain Monte Carlo sampling scheme. Simulation results obtained with both synthetic and real datasets are showing the outperformance of the proposed sparse Bayesian technique compared to other existing regularization techniques.

Item Type:Article
Additional Information:Thanks to Springer editor. This papers appears in Volume 14 of Signal, Image and Video Processing ISSN : 1863-1703 The original PDF is available at: https://link.springer.com/article/10.1007/s11760-019-01567-5
HAL Id:hal-02950816
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Digital Research Center of Sfax (TUNISIA)
Laboratory name:
Deposited On:22 Sep 2020 08:25

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