MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior.
Sensors (Basel)
; 20(11)2020 Jun 03.
Article
en En
| MEDLINE
| ID: mdl-32503338
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.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Clinical_trials
/
Health_economic_evaluation
Idioma:
En
Revista:
Sensors (Basel)
Año:
2020
Tipo del documento:
Article