Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20113185 | SENSORS |
Keywords | DocType | Volume |
magnetic resonance imaging,Markov random field,image reconstruction | Journal | 20 |
Issue | ISSN | Citations |
11 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marko Panić | 1 | 0 | 0.34 |
| Dusan Jakovetic | 2 | 345 | 25.15 |
| Dejan Vukobratović | 3 | 378 | 36.82 |
| Vladimir S. Crnojevic | 4 | 186 | 17.82 |
| Aleksandra Pizurica | 5 | 1238 | 102.29 |