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Abstract #1188

Automatic determination of the regularization weighting for wavelet-based compressed sensing MRI reconstructions 

Gabriel Varela-Mattatall1,2, Corey A Baron1,2, and Ravi S Menon1,2
1Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, Canada, 2Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada

Most compressed sensing reconstruction procedures have relied on user-defined tuning and/or multiple comparisons to the fully sampled data to demonstrate both the feasibility of compressed sensing reconstructions as the appropriate selection of the regularization weighting. Obviously, this is a time-consuming procedure which could be avoided if we had a method that provides the regularization weighting in an automatic, non-iterative, prospective, and fast manner. Here, we present such method that could significantly accelerate research that is based on compressed sensing and improve its clinical translatability when the sparsifying domain is based on the wavelet transform.

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