An End-to-End deep learning compressed sensing reconstruction model with adaptive shrinkage threshold

Motivation: Using deep learning methods to improve image quality and computation speed of compressed sensing reconstruction. Goal(s): Develop a model-based deep learning model with adaptive threshold selection module to improve the reconstruction quality. Approach: Introducing a new shrinkage function with adaptive threshold selection for Model-driven deep learning networks, and emploits End-to-End strategy for multicoil reconstruction. Results: Experiments demonstrate the efficacy of End-to-End reconstruction strategy with sensitivity reconstruction module, and show that proposed adaptive threshold selection method can effectively reduce reconstruction errors. Impact: We develop an End-to-End deep learning reconstruction network with adaptive threshold selection module. This network canenforce the performance of state-of-art model-based deep learning method for CS reconstruction, and achieve good reconstruction quality at R=8.