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.
@article{arxiv.2203.15725,
title = {Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey},
author = {Wenjun Xia and Hongming Shan and Ge Wang and Yi Zhang},
journal= {arXiv preprint arXiv:2203.15725},
year = {2023}
}