Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
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.
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