Advanced search
1 file | 10.76 MB Add to list

MRI reconstruction using Markov random field and total variation as composite prior

(2020) SENSORS. 20(11).
Author
Organization
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
Electrical and Electronic Engineering, Analytical Chemistry, Atomic and Molecular Physics, and Optics, Biochemistry

Downloads

  • sensors-20-03185-v2.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 10.76 MB

Citation

Please use this url to cite or link to this publication:

MLA
Panic, Marko, et al. “MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior.” SENSORS, vol. 20, no. 11, 2020, doi:10.3390/s20113185.
APA
Panic, M., Jakovetić, D., Vukobratović, D., Crnojević, V., & Pizurica, A. (2020). MRI reconstruction using Markov random field and total variation as composite prior. SENSORS, 20(11). https://doi.org/10.3390/s20113185
Chicago author-date
Panic, Marko, Dušan Jakovetić, Dejan Vukobratović, Vladimir Crnojević, and Aleksandra Pizurica. 2020. “MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior.” SENSORS 20 (11). https://doi.org/10.3390/s20113185.
Chicago author-date (all authors)
Panic, Marko, Dušan Jakovetić, Dejan Vukobratović, Vladimir Crnojević, and Aleksandra Pizurica. 2020. “MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior.” SENSORS 20 (11). doi:10.3390/s20113185.
Vancouver
1.
Panic M, Jakovetić D, Vukobratović D, Crnojević V, Pizurica A. MRI reconstruction using Markov random field and total variation as composite prior. SENSORS. 2020;20(11).
IEEE
[1]
M. Panic, D. Jakovetić, D. Vukobratović, V. Crnojević, and A. Pizurica, “MRI reconstruction using Markov random field and total variation as composite prior,” SENSORS, vol. 20, no. 11, 2020.
@article{8664006,
  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.},
  articleno    = {3185},
  author       = {Panic, Marko and Jakovetić, Dušan and Vukobratović, Dejan and Crnojević, Vladimir and Pizurica, Aleksandra},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keywords     = {Electrical and Electronic Engineering,Analytical Chemistry,Atomic and Molecular Physics,and Optics,Biochemistry},
  language     = {eng},
  number       = {11},
  pages        = {15},
  title        = {MRI reconstruction using Markov random field and total variation as composite prior},
  url          = {http://dx.doi.org/10.3390/s20113185},
  volume       = {20},
  year         = {2020},
}

Altmetric
View in Altmetric