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

Marko Panić, Dušan Jakovetić, Dejan Vukobratović, Vladimir Crnojević, Aleksandra Pižurica
2020 Zenodo  
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
more » ... l 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.
doi:10.5281/zenodo.3957268 fatcat:nxkjipsnrrft3lj3rlceojah34