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Abstract #3809

Improving low-rank plus sparse decomposition of dynamic MRI using short temporal snippets

Esben Plenge 1 , Tal Shnitzer 1 , and Michael Elad 1

1 Technion - Israel Institute of Technology, Haifa, Israel

In this study we present a new dictionary-based model and its application as sparsifying operator in a low-rank plus sparse matrix decomposition. According to the model, short temporal signals of a dynamic MRI sequence are sparse under a non-linear transformation using a trained dictionary. We validate the model, quantitatively and qualitatively in the context of reconstruction of under-sampled abdominal MRI using a numerical phantom and in vivo MRI data.

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