Dependent nonparametric bayesian group dictionary learning for online reconstruction of dynamic MR images
| Autor: | Jacob L. Jaremko, Dornoosh Zonoobi, Shahrooz Faghih Roohi, Ashraf A. Kassim |
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| Rok vydání: | 2017 |
| Předmět: |
Group (mathematics)
Computer science business.industry Dynamic mr Process (computing) Pattern recognition 02 engineering and technology Iterative reconstruction 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Compressed sensing Artificial Intelligence Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Nonparametric bayesian Computer Vision and Pattern Recognition Artificial intelligence business Dictionary learning Software |
| Zdroj: | Pattern Recognition. 63:518-530 |
| ISSN: | 0031-3203 |
| DOI: | 10.1016/j.patcog.2016.09.038 |
| Popis: | In this paper, we introduce a dictionary learning based approach applied to the problem of real-time reconstruction of MR image sequences that are highly undersampled in k-space. Unlike traditional dictionary learning, our method integrates both global and patch-wise (local) sparsity information and incorporates some priori information into the reconstruction process. Moreover, we use a Dependent Hierarchical Beta-process as the prior for the group-based dictionary learning, which adaptively infers the dictionary size and the sparsity of each patch; and also ensures that similar patches are manifested in terms of similar dictionary atoms. An efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) is also presented. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, compared to the other state-of-the- art DL-based methods. |
| Databáze: | OpenAIRE |
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