  Since 2016, deep learning (DL) has advanced tomographic imaging with
remarkable successes, especially in low-dose computed tomography (LDCT)
imaging. Despite being driven by big data, the LDCT denoising and pure
end-to-end reconstruction networks often suffer from the black box nature and
major issues such as instabilities, which is a major barrier to apply deep
learning methods in low-dose CT applications. An emerging trend is to integrate
imaging physics and model into deep networks, enabling a hybridization of
physics/model-based and data-driven elements. %This type of hybrid methods has
become increasingly influential. In this paper, we systematically review the
physics/model-based data-driven methods for LDCT, summarize the loss functions
and training strategies, evaluate the performance of different methods, and
discuss relevant issues and future directions.
