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
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
Here we present an automatic, non-iterative, prospective, and fast approach to determine the regularization weighting, which enables wavelet-based compressed sensing MRI reconstructions.
Knee images from the different strategies to define the regularization weighting. We compare (A) $$$ \lambda_{\textrm{Lcurve}} $$$ (14 iterations) and (B) $$$ \lambda_{\textrm{NMSE}} $$$ (14 iterations) strategies to (C) our approach, $$$ \lambda_{\textrm{auto}} $$$, using the 4th-level Daubechies mother wavelet (1 iteration). Panel (D) shows the reference and panel (E) shows quantitative results. Smaller values along each axis in (E) represent more accurate reconstructions.
Visualization of magnitude and phase images from simulations. Panels (B) and (E) show the images from the reconstruction process using our approach for USF=5 and $$$\sigma=5$$$. Panels (A) and (D) show the reference. Panels (C) and (F) show the difference between both columns. All images are scaled according to their respective reference.