LEARN: Learned Experts' Assessment-Based Reconstruction Network for Sparse-Data CT.

ABSTRACT: Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed tomography (CT), tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses 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 the 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 fea

SUBMITTER: Chen H 

PROVIDER: S-EPMC6019143 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature