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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