Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey
This is an incremental survey that synthesizes existing research to guide improvements in LDCT imaging for medical applications.
This survey reviews hybrid methods that integrate physics/model-based approaches with data-driven deep learning for low-dose computed tomography (LDCT) imaging, addressing issues like instability and black-box nature in existing methods, and systematically evaluates their performance and future directions.
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.