LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT

Understand

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on.

• To perform sparse-data CT, the iterative reconstruction commonly use regularizers in the CS framework.
• Currently, how to choose the parameters adaptively for regularization is a major open problem.
• In this paper, inspired by the idea of machine learning especially 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 our LEARN network.