Kedar Khare1, Christopher J. Hardy1,
Kevin F. King2, Patrick A. Turski3, Luca Marinelli1
1GE Global Research Center, Niskayuna,
NY, United States; 2GE Healthcare, Waukesha, WI, United States; 3School
of Medicine & Public Health, University of Wisconsin, Madison, WI, United
States
We
present a robust method for compressed-sensing reconstruction using a data-driven,
iterative soft-thresholding (ST) framework with no tuning of free parameters.
The algorithm combines a Nesterov-type optimal gradient scheme for iterative
update with adaptive wavelet denoising methods. Vascular 3D phase-contrast
scans on healthy volunteers are used to show that image quality is comparable
to that of empirically tuned, nonlinear conjugate-gradient (NLCG)
reconstruction. Statistical analysis of image quality scores for five
datasets indicates that the ST approach improves the robustness of the
reconstruction and image quality as compared to NLCG with a single set of
tuning parameters for all scans.