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Abstract #3414

Parameter-Free Sparsity Adaptive Compressive Recovery (SCoRe)

Rizwan Ahmad 1 , Philip Schniter 1 , and Orlando P. Simonetti 2

1 Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, United States, 2 Internal Medicine and Radiology, The Ohio State University, Columbus, Ohio, United States

Redundant dictionaries are routinely used to exploit rich structure in MR images. When using a redundant dictionary, however, the level of sparsity may vary across different groups of atoms, i.e., across subdictionaries. In this work, we propose a method, called Sparsity Adaptive Compressive Recovery (SCoRe), that adapts to the inherent level of sparsity in each subdictionary. Moreover, the proposed adaptation is data-driven and does not introduce any tuning parameters. For validation, results from digital phantom and real-time cine are presented.

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