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Abstract #1186

Automatic WaveCS reconstruction

Gabriel Varela-Mattatall1,2 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

WaveCS is the combination of corkscrew trajectories in k-space with the application of compressed sensing reconstruction. However, its accurate reconstruction depends critically on user-defined tuning. This is a tedious process that may require additional optimization if acquisition parameters are changed. Furthermore, an incorrect regularization weighting could generate noise amplification, emergence of artifacts, smoothing and loss of structural information. Here, we present a fast, non-iterative and automatic procedure that estimates the regularization weighting and which reconstruction is comparable to previous reconstructions using more tedious approaches that are considered the state-of-the-art.

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