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MR image reconstruction by patch-based sparse representation.

Author(s)
Qin Lin Department of Communication Engineering, Xiamen University, Fujian, China
Wei Guo Department of Communication Engineering, Xiamen University, Fujian, China
Xueyang Fu Department of Communication Engineering, Xiamen University, Fujian, China
Xinghao Ding Department of Communication Engineering, Xiamen University, Fujian, China
Yue Huang Department of Communication Engineering, Xiamen University, Fujian, China
Abstract
Compressed sensing has been widely used to reconstruct magnetic resonance (MR) images from highly under-sampled k-space data. Generally, there are two main types of dictionary: analytic dictionary and trained dictionary. In this paper, we propose a novel framework to adaptively learn the dictionary and reconstruct MR images from highly under-sampled k-space data. We use the beta-Bernoulli process as a Bayesian nonparametric prior, which can learn dictionary adaptively. This patch-based dictionary learning process can also infers the sparsity of each patch and the noise variance. Our numerical experiments demonstrate that our reconstruction is more accurate compared to existing algorithms.
Publication Details
Page(s) 107-112
DOI DOI not available
Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 49, Issue: 1, Year: 2013

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