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To perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS framework. In this paper, inspired by the idea of deep learning, we unfold a state-of-the-art “fields of experts” based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a Learned Experts’ Assessment-based Reconstruction Network (“LEARN”) for sparse-data CT, and demonstrate the feasibility and merits of LEARN network. The experimental results produces a superior performance to several state-of-the-art methods.
This presentation is part of Minisymposium “MS24 - Data-driven approaches in imaging science (3 parts)”
organized by: Jae Kyu Choi (Institute of Natural Sciences, Shanghai Jiao Tong University) , Chenglong Bao (Yau Mathematical Sciences Center, Tsinghua University) .