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Proceedings Paper

Multi-coil magnetic resonance imaging reconstruction with a Markov random field prior
Author(s): Marko Panić; Jan Aelterman; Vladimir Crnojević; Aleksandra Pižurica
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Paper Abstract

Recent improvements in magnetic resonance image (MRI) reconstruction from partial data have been reported using spatial context modelling with Markov random field (MRF) priors. However, these algorithms have been developed only for magnitude images from single-coil measurements. In practice, most of the MRI images today are acquired using multi-coil data. In this paper, we extend our recent approach for MRI reconstruction with MRF priors to deal with multi-coil data i.e., to be applicable in parallel MRI (pMRI) settings. Instead of reconstructing images from different coils independently and subsequently combining them into the final image, we recover MRI image by processing jointly the undersampled measurements from all coils together with their estimated sensitivity maps. The proposed method incorporates a Bayesian formulation of the spatial context into the reconstruction problem. To solve the resulting problem, we derive an efficient algorithm based on the alternating direction method of multipliers (ADMM). Experimental results demonstrate the effectiveness of the proposed approach in comparison to some well-adopted methods for accelerated pMRI reconstruction from undersampled data.

Paper Details

Date Published: 15 March 2019
PDF: 10 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492G (15 March 2019); doi: 10.1117/12.2512104
Show Author Affiliations
Marko Panić, BioSense Institute (Serbia)
Jan Aelterman, Univ. Gent (Belgium)
Vladimir Crnojević, BioSense Institute (Serbia)
Aleksandra Pižurica, Univ. Gent (Belgium)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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