A deep learning approach for compressed sensing reconstruction using adaptive shrinkage threshold

Compressed Sensing is widely used for accelerating acquisitions of MR images. Deep learning methods have been introduced to CS-MRI reconstruction to improve image quality and computation speed. Here we introduce a new shrinkage function with adaptive threshold selection for Model-driven deep learning networks to suppress aliasing artifacts by utilizing the information on each feature map. We combine our adaptive threshold selection module with ISTA-Net, and demonstrate that the method can reduce reconstruction errors while preserving structural details effectively.