Improving low-rank plus sparse decomposition of dynamic MRI using short temporal snippets
Esben Plenge 1 , Tal Shnitzer 1 , and Michael Elad 1
Technion - Israel Institute of Technology,
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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