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https://open.uns.ac.rs/handle/123456789/4346
Nаziv: | Compressed sensing in MRI with a Markov random field prior for spatial clustering of subband coefficients | Аutоri: | Panić, Marko Aelterman, Jan Crnojević, Vladimir Pižurica, Aleksandra |
Dаtum izdаvаnjа: | авг-2016 | Čаsоpis: | European Signal Processing Conference | Sažetak: | © 2016 IEEE. Recent work in compressed sensing of magnetic resonance images (CS-MRI) concentrates on encoding structured sparsity in acquisition or in the reconstruction stages. Subband coefficients of typical images obey a certain structure, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Approaches using wavelet tree-sparsity have already demonstrated excellent performance in MRI. However, the use of statistical models for spatial clustering of the subband coefficients has not been studied well in CS-MRI yet, although the potentials of such an approach have been indicated. In this paper, we design a practical reconstruction algorithm as a variant of the proximal splitting methods, making use of a Markov Random Field prior model for spatial clustering of subband coefficients. The results for different undersampling patterns demonstrate an improved reconstruction performance compared to both standard CS-MRI methods and methods based on wavelet tree sparsity. | URI: | https://open.uns.ac.rs/handle/123456789/4346 | ISBN: | 9780992862657 | ISSN: | 22195491 | DOI: | 10.1109/EUSIPCO.2016.7760311 |
Nаlаzi sе u kоlеkciјаmа: | IBS Publikacije/Publications TF Publikacije/Publications |
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