@article{10.1371/journal.pone.0184667,
    doi = {10.1371/journal.pone.0184667},
    author = {Cui, Jianan AND Liu, Xin AND Wang, Yile AND Liu, Huafeng},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Deep reconstruction model for dynamic PET images},
    year = {2017},
    month = {09},
    volume = {12},
    url = {https://doi.org/10.1371/journal.pone.0184667},
    pages = {1-21},
    abstract = {Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the activity distribution-based on individual frames, may lead to inaccurate results due to the checkerboard effect and limitation of photon counts. In this paper, we propose a stacked sparse auto-encoder based reconstruction framework for dynamic PET imaging. The dynamic reconstruction problem is formulated in a deep learning representation, where the encoding layers extract the prototype features, such as edges, so that, in the decoding layers, the reconstructed results are obtained through a combination of those features. The qualitative and quantitative results of the procedure, including the data based on a Monte Carlo simulation and real patient data demonstrates the effectiveness of our method.},
    number = {9},

}