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Compressive sensing (CS) has proved effective for tomographic reconstructionfrom sparsely collected data or under-sampled measurements, which arepractically important for few-view CT, tomosynthesis, interior tomography, andso on. To perform sparse-data CT, the iterative reconstruction commonly useregularizers in the CS framework. Currently, how to choose the parametersadaptively for regularization is a major open problem. In this paper, inspiredby the idea of machine learning especially deep learning, we unfold astate-of-the-art "fields of experts" based iterative reconstruction scheme upto a number of iterations for data-driven training, construct a LearnedExperts' Assessment-based Reconstruction Network ("LEARN") for sparse-data CT,and demonstrate the feasibility and merits of our LEARN network. Theexperimental results with our proposed LEARN network produces a competitiveperformance with the well-known Mayo Clinic Low-Dose Challenge Dataset relativeto several state-of-the-art methods, in terms of artifact reduction, featurepreservation, and computational speed. This is consistent to our insight thatbecause all the regularization terms and parameters used in the iterativereconstruction are now learned from the training data, our LEARN networkutilizes application-oriented knowledge more effectively and recoversunderlying images more favorably than competing algorithms. Also, the number oflayers in the LEARN network is only 12, reducing the computational complexityof typical iterative algorithms by orders of magnitude.

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