Adaptive threshold selection for compressed sensing reconstruction

Motivation: The reconstruction quality of CS-MRI is significantly affected by the selection of shrinkage threshold. Goal(s): Find a self-adaptive threshold for every iteration, every slice and every wavelet sub-band in compressed sensing reconstruction. Approach: We propose an adaptive threshold selection method by combining an bayes-based adaptive wavelet shrinkage denoising method with compressed sensing reconstruction. Results: Our threshold based on the coefficients in sparse transform domain has a better reconstruction performance compared with an optimal fixed threshold. Impact: We propose an adaptive threshold selection method for compressed sensing reconstruction, which promote the reconstruction quality and avoid the manual selection of parameter.