MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior

Marko Panić; Dušan Jakovetić; Dejan Vukobratović; Vladimir Crnojević; Aleksandra Pižurica

doi:10.3390/s20113185

MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior

Sensors (Basel). 2020 Jun 3;20(11):3185. doi: 10.3390/s20113185.

Authors

Marko Panić¹, Dušan Jakovetić², Dejan Vukobratović³, Vladimir Crnojević¹, Aleksandra Pižurica⁴

Affiliations

¹ BioSense Institute, University of Novi Sad, 21000 Novi Sad, Serbia.
² Faculty of Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.
³ Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.
⁴ Department of Telecommunications and Information Processing, Ghent University, 9000 Ghent, Belgium.

Abstract

Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field.

Keywords: Markov random field; image reconstruction; magnetic resonance imaging.