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- W2284001077 abstract "Dynamic magnetic resonance imaging (dMRI) is widely used in human motion organ and functional imaging. But it requires reducing the imaging time to obtain high spatial and temporal resolution. This paper proposes a double sparse spatiotemporal dictionary model for compressed sensing reconstruction of dMRI from undersampled data. The model extends the ordinary 2-D dictionary to 3-D spatiotemporal dictionary by sparse representation of both the signals and dictionary atoms. Specifically, the first level sparse representation of dictionary atoms is learned with K-SVD algorithm. The second level sparse representation of spatiotemporal patches is obtained by OMP algorithm. An alternate iterative optimization is applied to solve the problem. Experiment results demonstrate that comparing with the state of the art method k-t FOCUSS and single level dictionary learning – DLMRI, the proposed method performs better in removing aliasing artifacts and in capturing temporal variations as well." @default.
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- W2284001077 date "2015-01-01" @default.
- W2284001077 modified "2023-09-22" @default.
- W2284001077 title "Undersampled Dynamic MRI Reconstruction by Double Sparse Spatiotemporal Dictionary" @default.
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- W2284001077 doi "https://doi.org/10.1007/978-3-319-21969-1_45" @default.
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